{"id":13633402,"url":"https://github.com/chaoyanghe/Awesome-Federated-Learning","last_synced_at":"2025-04-18T10:34:49.754Z","repository":{"id":37387346,"uuid":"276973067","full_name":"chaoyanghe/Awesome-Federated-Learning","owner":"chaoyanghe","description":"FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai","archived":false,"fork":false,"pushed_at":"2022-09-03T20:03:02.000Z","size":215,"stargazers_count":1879,"open_issues_count":2,"forks_count":323,"subscribers_count":90,"default_branch":"master","last_synced_at":"2024-05-19T18:05:53.328Z","etag":null,"topics":["adversarial-attack-and-defense","communication-efficiency","computation-efficiency","computer-vision","continual-learning","decentralized-federated-learning","distributed-optimization","federated-learning","hierarchical-federated-learning","incentive-mechanism","interpretability","machine-learning","neural-architecture-search","non-iid","privacy","semi-supervised-learning","straggler-problem","transfer-learning","vertical-federated-learning","wireless-communication"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/chaoyanghe.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-07-03T19:26:03.000Z","updated_at":"2024-05-18T09:04:45.000Z","dependencies_parsed_at":"2022-07-08T04:56:35.103Z","dependency_job_id":null,"html_url":"https://github.com/chaoyanghe/Awesome-Federated-Learning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chaoyanghe%2FAwesome-Federated-Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chaoyanghe%2FAwesome-Federated-Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chaoyanghe%2FAwesome-Federated-Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chaoyanghe%2FAwesome-Federated-Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/chaoyanghe","download_url":"https://codeload.github.com/chaoyanghe/Awesome-Federated-Learning/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":223779725,"owners_count":17201220,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["adversarial-attack-and-defense","communication-efficiency","computation-efficiency","computer-vision","continual-learning","decentralized-federated-learning","distributed-optimization","federated-learning","hierarchical-federated-learning","incentive-mechanism","interpretability","machine-learning","neural-architecture-search","non-iid","privacy","semi-supervised-learning","straggler-problem","transfer-learning","vertical-federated-learning","wireless-communication"],"created_at":"2024-08-01T23:00:36.801Z","updated_at":"2024-11-09T02:32:08.457Z","avatar_url":"https://github.com/chaoyanghe.png","language":null,"funding_links":[],"categories":["Profiling","Others","Table of Contents","Uncategorized","分布式机器学习","Awesome Lists","Other Lists","acknowledgments","Related Awesome Lists"],"sub_categories":["Uncategorized","Profiling","TeX Lists","secret sharing","Control Electronics"],"readme":"\u003cspan style=\"color:red\"\u003eThe latest update has been moved to\u003c/span\u003e https://github.com/FedML-AI/FedML/blob/master/research/Awesome-Federated-Learning.md\n\n## Federated Learning Library - FedML https://fedml.ai\n\n# Awesome-Federated-Learning [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)\n\nA curated list of federated learning publications, re-organized from Arxiv (mostly).\n\n\u003cstrong\u003eLast Update: July, 20th, 2021\u003c/strong\u003e.\t\n\nIf your publication is not included here, please email to chaoyanghe.com@gmail.com\n\n# Foundations and Trends in Machine Learning\nWe are thrilled to share that [Advances and Open Problems in Federated Learning](https://arxiv.org/abs/1912.04977) has been accepted to [FnTML](https://www.nowpublishers.com/MAL) (\u003cb\u003eFoundations and Trends in Machine Learning\u003c/b\u003e, the chief editor is [Michael Jordan](https://people.eecs.berkeley.edu/~jordan/)).\n\n[A Field Guide to Federated Optimization](https://arxiv.org/abs/2107.06917)\n\n\n## Publications in Top-tier ML/CV/NLP/DM Conference (ICML, NeurIPS, ICLR, CVPR, ACL, AAAI, KDD)\n### ICML\n| Title                                                                    | Team/Authors              | Venue and Year     | Targeting Problem     | Method                |\n|---|---|---|---|---|\n| [Federated Learning with Only Positive Labels](https://arxiv.org/pdf/2004.10342.pdf)                        | Google Research            |   ICML 2020        | label deficiency in multi-class classification    |  regularization |\n| [SCAFFOLD: Stochastic Controlled Averaging for Federated Learning](https://arxiv.org/abs/1910.06378)        | EPFL, Google Research      |   ICML 2020        | heterogeneous data (non-I.I.D)    | nonconvex/convex optimization with variance reduction   |\n| [FedBoost: A Communication-Efficient Algorithm for Federated Learning](https://proceedings.icml.cc/static/paper_files/icml/2020/5967-Paper.pdf)    | Google Research, NYU       |   ICML 2020        | communication cost    | ensemble algorithm    |\n| [FetchSGD: Communication-Efficient Federated Learning with Sketching](https://arxiv.org/abs/2007.07682)     | UC Berkeley, JHU, Amazon   |   ICML 2020        | communication cost    | compress model updates with Count Sketch   |\n| [From Local SGD to Local Fixed-Point Methods for Federated Learning](https://arxiv.org/pdf/2004.01442.pdf)  | KAUST                      |   ICML 2020        | communication cost    |  Optimization |\n\n### NeurIPS\n| Title                                                                    | Team/Authors              | Venue and Year     | Targeting Problem     | Method                |\n|---|---|---|---|---|\n| Lower Bounds and Optimal Algorithms for Personalized Federated Learning | KAUST   | NeurIPS 2020        |  non-I.I.D, personalization   |   |\n| Personalized Federated Learning with Moreau Envelopes  | The University of Sydney | NeurIPS 2020        |  non-I.I.D, personalization   |   |\n| Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach | MIT |   NeurIPS 2020        |  non-I.I.D, personalization   |   |\n| Differentially-Private Federated Contextual Bandits                     | MIT            |   NeurIPS 2020        |  Contextual Bandits   |   |\n| Federated Principal Component Analysis                     | Cambridge            |   NeurIPS 2020        |    PCA |   |\n| FedSplit: an algorithmic framework for fast federated optimization                     | UCB            |   NeurIPS 2020        |   Acceleration  |   |\n| Federated Bayesian Optimization via Thompson Sampling | MIT |   NeurIPS 2020        |     |   |\n| Robust Federated Learning: The Case of Affine Distribution Shifts | MIT  | NeurIPS 2020        |   Privacy, Robustness  |   |\n| An Efficient Framework for Clustered Federated Learning | UCB | NeurIPS 2020        |    heterogeneous data (non-I.I.D) |   |\n| Distributionally Robust Federated Averaging | PSU |   NeurIPS 2020        |  Privacy, Robustness   |   |\n| Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge | USC |   NeurIPS 2020        |  Efficient Training of Large DNN at Edge   |   |\n| A Scalable Approach for Privacy-Preserving Collaborative Machine Learning  | USC |   NeurIPS 2020        |  Scalability   |   |\n| Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization | CMU |   NeurIPS 2020        |   local update step heterogeneity  |   |\n| Attack of the Tails: Yes, You Really Can Backdoor Federated Learning | Wiscosin|   NeurIPS 2020        |  Privacy, Robustness   |   |\n| Federated Accelerated Stochastic Gradient Descent | Stanford |   NeurIPS 2020        |  Acceleration   |   |\n| Inverting Gradients - How easy is it to break privacy in federated learning? | University of Siegen   | NeurIPS 2020        |  Privacy, Robustness   |   |\n| Ensemble Distillation for Robust Model Fusion in Federated Learning  | EPFL |   NeurIPS 2020        |   Privacy, Robustness  |   |\n| Optimal Topology Design for Cross-Silo Federated Learning  | Inria | NeurIPS 2020        | Topology Optimization    |   |\n| Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms | University of Minnesota | NeurIPS 2020 | | | \n| Distributed Distillation for On-Device Learning | Stanford | NeurIPS 2020 | | |\n| Byzantine Resilient Distributed Multi-Task Learning | Vanderbilt University | NeurIPS 2020 | | | \n| Distributed Newton Can Communicate Less and Resist Byzantine Workers | UCB | NeurIPS 2020 | | |\n| Minibatch vs Local SGD for Heterogeneous Distributed Learning | TTIC | NeurIPS 2020 | | |\n| Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks | | NeurIPS 2020 | | |\n\n(according to https://neurips.cc/Conferences/2020/AcceptedPapersInitial)\n\nNote: most of the accepted publications are preparing the camera ready revision, thus we are not sure the detail of their proposed methods\n\n\n## Research Areas\n#### Statistical Challenges: data distribution heterogeneity and label deficiency (159)\n - [Distributed Optimization](#Distributed-optimization (68))\n - [Non-IID and Model Personalization](#Non-IID-and-Model-Personalization (53))\n - [Semi-Supervised Learning](#Semi-Supervised-Learning (3))\n - [Vertical Federated Learning](#Vertical-Federated-Learning (8))\n - [Decentralized FL](#Decentralized-FL (7))\n - [Hierarchical FL](#Hierarchical-FL (8))\n - [Neural Architecture Search](#Neural-Architecture-Search (4))\n - [Transfer Learning](#Transfer-Learning (11))\n - [Continual Learning](#continual-learning (1))\n - [Domain Adaptation](#Domain-Adaptation)\n - [Reinforcement Learning](#Reinforcement-Learning)\n - [Bayesian Learning ](#Bayesian-Learning )\n - [Causal Learning](#Causal-Learning )\n\n\n#### Trustworthiness: security, privacy, fairness, incentive mechanism, etc. (88)\n - [Adversarial-Attack-and-Defense](#Adversarial-Attack-and-Defense)\n - [Privacy](#Privacy (36))\n - [Fairness](#Fairness (4))\n - [Interpretability](#Interpretability)\n - [Incentive Mechanism](#Incentive-Mechanism (5))\n\n#### System Challenges: communication and computational resource constrained, software and hardware heterogeneity, and FL system (141)\n - [Communication-Efficiency](#Communication-Efficiency (29))\n - [Straggler Problem](#straggler-problem (4))\n - [Computation Efficiency](#Computation-Efficiency (14))\n - [Wireless Communication and Cloud Computing](#Wireless-Communication-and-Cloud-Computing (74))\n - [FL System Design](#FL-System-Design (20))\n\n#### Models and Applications (104)\n - [Models](#Models (22))\n - [Natural language Processing](#Natural-language-Processing (15))\n - [Computer Vision](#Computer-Vision (3))\n - [Health Care](#Health-Care (27))\n - [Transportation](#Transportation (14))\n - [Recommendation System](#Recommendation-System (8))\n - [Speech](#Speech (1))\n - [Finance](#Finance (2))\n - [Smart City](#Smart-City (2))\n - [Robotics](#Robotics (2))\n - [Networking](#Networking (1))\n - [Blockchain](#Blockchain (2))\n - [Other](#Other (5))\n \n#### Benchmark, Dataset and Survey (27)\n - [Benchmark and Dataset](#Benchmark-and-Dataset)  (7)\n - [Survey](#Survey) (20)\n\n-------------------\n\n# Statistical Challenges: distribution heterogeneity and label deficiency \n\n## Distributed optimization\n\u003cspan style=\"color:blue\"\u003eUserful Federated Optimizer Baselines:\u003c/span\u003e\n\nFedAvg:\n[Communication-Efficient Learning of Deep Networks from Decentralized Data. 2016-02. AISTAT 2017.](https://arxiv.org/pdf/1602.05629.pdf)\n\nFedOpt:\n[Adaptive Federated Optimization. ICLR 2021 (Under Review). 2020-02-29](https://arxiv.org/pdf/2003.00295.pdf)\n\nFedNov:\n[Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization. NeurIPS 2020](https://arxiv.org/abs/2007.07481)\n\n-------------------------\n\n[Federated Optimization: Distributed Optimization Beyond the Datacenter. NIPS 2016 workshop.](https://arxiv.org/pdf/1511.03575.pdf)\n\n[Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://arxiv.org/pdf/1610.02527.pdf)\n\n[Stochastic, Distributed and Federated Optimization for Machine Learning. FL PhD Thesis. By Jakub](https://arxiv.org/pdf/1707.01155.pdf)\n\n[Collaborative Deep Learning in Fixed Topology Networks](https://arxiv.org/pdf/1706.07880.pdf)\n\n[Federated Multi-Task Learning](https://arxiv.org/pdf/1705.10467.pdf)\n\n[LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning](https://arxiv.org/abs/1805.09965)\n\n[Local Stochastic Approximation: A Unified View of Federated Learning and Distributed Multi-Task Reinforcement Learning Algorithms](https://arxiv.org/pdf/2006.13460.pdf)\n\n[Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning](https://arxiv.org/pdf/2005.06105.pdf)\n\n[Exact Support Recovery in Federated Regression with One-shot Communication](https://arxiv.org/pdf/2006.12583.pdf)\n\n[DEED: A General Quantization Scheme for Communication Efficiency in Bits](https://arxiv.org/pdf/2006.11401.pdf)\nResearcher: Ruoyu Sun, UIUC\n\n[Robust Federated Learning: The Case of Affine Distribution Shifts](https://arxiv.org/pdf/2006.08907.pdf)\n\n[Personalized Federated Learning with Moreau Envelopes](https://arxiv.org/pdf/2006.08848.pdf)\n\n[Towards Flexible Device Participation in Federated Learning for Non-IID Data](https://arxiv.org/pdf/2006.06954.pdf)\nKeywords: inactive or return incomplete updates in non-IID dataset\n\n[A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization](https://arxiv.org/pdf/2006.03474.pdf)\n\n[FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data](https://arxiv.org/pdf/2005.11418.pdf)\nResearcher: Wotao Yin, UCLA\n\n[FedSplit: An algorithmic framework for fast federated optimization](https://arxiv.org/pdf/2005.05238.pdf)\n\n[Distributed Stochastic Non-Convex Optimization: Momentum-Based Variance Reduction](https://arxiv.org/pdf/2005.00224.pdf)\n\n[On the Outsized Importance of Learning Rates in Local Update Methods](https://arxiv.org/pdf/2007.00878.pdf)\nHighlight: local model learning rate optimization + automation\nResearcher: Jakub\n\n[Federated Learning with Compression: Unified Analysis and Sharp Guarantees](https://arxiv.org/pdf/2007.01154.pdf)\nHighlight: non-IID, gradient compression + local SGD\nResearcher: Mehrdad Mahdavi, Jin Rong’s PhD Student http://www.cse.psu.edu/~mzm616/\n\n[From Local SGD to Local Fixed-Point Methods for Federated Learning](https://arxiv.org/pdf/2004.01442.pdf)\n\n[Federated Residual Learning. 2020-03](https://arxiv.org/pdf/2003.12880.pdf)\n\n\n[Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization. ICML 2020.](https://arxiv.org/pdf/2002.11364.pdf)\n\n[LASG: Lazily Aggregated Stochastic Gradients for Communication-Efficient Distributed Learning](https://arxiv.org/pdf/2002.11360.pdf)\n\n[Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor](https://arxiv.org/pdf/2002.08958.pdf)\n\n[Dynamic Federated Learning](https://arxiv.org/pdf/2002.08782.pdf)\n\n[Distributed Optimization over Block-Cyclic Data](https://arxiv.org/pdf/2002.07454.pdf)\n\n[Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability](https://arxiv.org/pdf/2002.07399.pdf)\n\n[Federated Learning with Matched Averaging](https://arxiv.org/pdf/2002.06440.pdf)\n\n[Federated Learning of a Mixture of Global and Local Models](https://arxiv.org/pdf/2002.05516.pdf)\n\n[Faster On-Device Training Using New Federated Momentum Algorithm](https://arxiv.org/pdf/2002.02090.pdf)\n\n[FedDANE: A Federated Newton-Type Method](https://arxiv.org/pdf/2001.01920.pdf)\n\n[Distributed Fixed Point Methods with Compressed Iterates](https://arxiv.org/pdf/1912.09925.pdf)\n\n[Primal-dual methods for large-scale and distributed convex optimization and data analytics](https://arxiv.org/pdf/1912.08546.pdf)\n\n[Parallel Restarted SPIDER - Communication Efficient Distributed Nonconvex Optimization with Optimal Computation Complexity](https://arxiv.org/pdf/1912.06036.pdf)\n\n[Representation of Federated Learning via Worst-Case Robust Optimization Theory](https://arxiv.org/pdf/1912.05571.pdf)\n\n[On the Convergence of Local Descent Methods in Federated Learning](https://arxiv.org/pdf/1910.14425.pdf)\n\n[SCAFFOLD: Stochastic Controlled Averaging for Federated Learning](https://arxiv.org/pdf/1910.06378.pdf)\n\n[Central Server Free Federated Learning over Single-sided Trust Social Networks](https://arxiv.org/pdf/1910.04956.pdf)\n\n[Accelerating Federated Learning via Momentum Gradient Descent](https://arxiv.org/pdf/1910.03197.pdf)\n\n[Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction](https://arxiv.org/pdf/1909.05844.pdf)\n\n[Gradient Descent with Compressed Iterates](https://arxiv.org/pdf/1909.04716.pdf)\n\n[First Analysis of Local GD on Heterogeneous Data](https://arxiv.org/pdf/1909.04715.pdf)\n\n[(*) On the Convergence of FedAvg on Non-IID Data. ICLR 2020.](https://arxiv.org/pdf/1907.02189.pdf)\n\n[Robust Federated Learning in a Heterogeneous Environment](https://arxiv.org/pdf/1906.06629.pdf)\n\n[Scalable and Differentially Private Distributed Aggregation in the Shuffled Model](https://arxiv.org/pdf/1906.08320.pdf)\n\n[Variational Federated Multi-Task Learning](https://arxiv.org/pdf/1906.06268.pdf)\n\n[Bayesian Nonparametric Federated Learning of Neural Networks. ICLR 2019.](https://arxiv.org/pdf/1905.12022.pdf)\n\n[Differentially Private Learning with Adaptive Clipping](https://arxiv.org/pdf/1905.03871.pdf)\n\n[Semi-Cyclic Stochastic Gradient Descent](https://arxiv.org/pdf/1904.10120.pdf)\n\n[Asynchronous Federated Optimization](https://arxiv.org/pdf/1903.03934.pdf)\n\n[Agnostic Federated Learning](https://arxiv.org/pdf/1902.00146.pdf)\n\n[Federated Optimization in Heterogeneous Networks](https://arxiv.org/pdf/1812.06127.pdf)\n\n[Partitioned Variational Inference: A unified framework encompassing federated and continual learning](https://arxiv.org/pdf/1811.11206.pdf)\n\n[Learning Rate Adaptation for Federated and Differentially Private Learning](https://arxiv.org/pdf/1809.03832.pdf)\n\n[Communication-Efficient Robust Federated Learning Over Heterogeneous Datasets](https://arxiv.org/pdf/2006.09992.pdf)\n\n[An Efficient Framework for Clustered Federated Learning](https://arxiv.org/pdf/2006.04088.pdf)\n\n[Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://arxiv.org/pdf/1804.05271.pdf)\nCitation: 146\n\n[Adaptive Federated Optimization](http://arxiv.org/pdf/2003.00295.pdf)\n\n[Local SGD converges fast and communicates little](https://arxiv.org/pdf/1805.09767.pdf)\n\n[Don’t Use Large Mini-Batches, Use Local SGD](https://arxiv.org/pdf/1808.07217.pdf)\n\n[Overlap Local-SGD: An Algorithmic Approach to Hide Communication Delays in Distributed SGD](https://arxiv.org/pdf/2002.09539.pdf)\n\n[Local SGD With a Communication Overhead Depending Only on the Number of Workers](https://arxiv.org/pdf/2006.02582.pdf)\n\n[Federated Accelerated Stochastic Gradient Descent ](https://arxiv.org/pdf/2006.08950.pdf)\n\n[Tighter Theory for Local SGD on Identical and Heterogeneous Data](https://arxiv.org/pdf/1909.04746.pdf)\n\n[STL-SGD: Speeding Up Local SGD with Stagewise Communication Period](https://arxiv.org/pdf/2006.06377.pdf)\n\n[Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms](https://arxiv.org/pdf/1808.07576.pdf)\n\n[Don't Use Large Mini-Batches, Use Local SGD](https://arxiv.org/pdf/1808.07217.pdf)\n\n[Understanding Unintended Memorization in Federated Learning](http://arxiv.org/pdf/2006.07490.pdf)\n\n## Non-IID and Model Personalization\n[The Non-IID Data Quagmire of Decentralized Machine Learning. 2019-10](https://arxiv.org/pdf/1910.00189.pdf)\n\n[Federated Learning with Non-IID Data](https://arxiv.org/pdf/1806.00582.pdf)\n\n[FedCD: Improving Performance in non-IID Federated Learning. 2020](https://arxiv.org/pdf/2006.09637.pdf)\n\n[Life Long Learning: FedFMC: Sequential Efficient Federated Learning on Non-iid Data. 2020](https://arxiv.org/pdf/2006.10937.pdf)\n\n[Robust Federated Learning: The Case of Affine Distribution Shifts. 2020](https://arxiv.org/pdf/2006.08907.pdf)\n\n[Personalized Federated Learning with Moreau Envelopes. 2020](https://arxiv.org/pdf/2006.08848.pdf)\n\n\n[Personalized Federated Learning using Hypernetworks. 2021](https://arxiv.org/pdf/2103.04628.pdf)\n\n[Ensemble Distillation for Robust Model Fusion in Federated Learning. 2020](https://arxiv.org/pdf/2006.07242.pdf)\nResearcher: Tao Lin, ZJU, EPFL https://tlin-tao-lin.github.io/index.html\n\n[Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning. 2020](https://arxiv.org/pdf/2005.06105.pdf)\n\n[Towards Flexible Device Participation in Federated Learning for Non-IID Data. 2020](https://arxiv.org/pdf/2006.06954.pdf)\nKeywords: inactive or return incomplete updates in non-IID dataset\n\n[XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning. 2020](https://arxiv.org/pdf/2006.05148.pdf)\n\n[NeurIPS 2020 submission: An Efficient Framework for Clustered Federated Learning. 2020](https://arxiv.org/pdf/2006.04088.pdf)\nResearcher: AVISHEK GHOSH, UCB, PhD\n\n[Continual Local Training for Better Initialization of Federated Models. 2020](https://arxiv.org/pdf/2005.12657.pdf)\n\n[FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data. 2020](https://arxiv.org/pdf/2005.11418.pdf)\nResearcher: Wotao Yin, UCLA\n\n[Global Multiclass Classification from Heterogeneous Local Models. 2020](https://arxiv.org/pdf/2005.10848.pdf)\nResearcher: Stanford https://stanford.edu/~pilanci/\n\n[Multi-Center Federated Learning. 2020](https://arxiv.org/pdf/2005.01026.pdf)\n\n[Federated learning with hierarchical clustering of local updates to improve training on non-IID data. 2020](https://arxiv.org/pdf/2004.11791.pdf)\n\n[Federated Learning with Only Positive Labels. 2020](https://arxiv.org/pdf/2004.10342.pdf)\nResearcher: Felix Xinnan Yu, Google New York\nKeywords: positive labels\nLimited Labels\n\n[Federated Semi-Supervised Learning with Inter-Client Consistency. 2020](https://arxiv.org/pdf/2006.12097.pdf)\n\n[(*) FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated Learning. CMU ECE. 2020-04-07](https://arxiv.org/pdf/2004.03657.pdf)\n\n[(*) Adaptive Personalized Federated Learning](https://arxiv.org/pdf/2003.13461.pdf)\n\n[Semi-Federated Learning](https://arxiv.org/pdf/2003.12795.pdf)\n\n[Survey of Personalization Techniques for Federated Learning. 2020-03-19](https://arxiv.org/pdf/2003.08673.pdf)\n\n[Device Heterogeneity in Federated Learning: A Superquantile Approach. 2020-02](https://arxiv.org/pdf/2002.11223.pdf)\n\n[Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework](https://arxiv.org/pdf/2002.10671.pdf)\n\n[Three Approaches for Personalization with Applications to Federated Learning](https://arxiv.org/pdf/2002.10619.pdf)\n\n[Personalized Federated Learning: A Meta-Learning Approach](https://arxiv.org/pdf/2002.07948.pdf)\n\n[Towards Federated Learning: Robustness Analytics to Data Heterogeneity](https://arxiv.org/pdf/2002.05038.pdf)\nHighlight: non-IID + adversarial attacks\n\n[Salvaging Federated Learning by Local Adaptation](https://arxiv.org/pdf/2002.04758.pdf)\nHighlight: an experimental paper that evaluate FL can help to improve the local accuracy\n\n[FOCUS: Dealing with Label Quality Disparity in Federated Learning. 2020-01](https://arxiv.org/pdf/2001.11359.pdf)\n\n[Overcoming Noisy and Irrelevant Data in Federated Learning. ICPR 2020.](https://arxiv.org/pdf/2001.08300.pdf)\n\n[Real-Time Edge Intelligence in the Making: A Collaborative Learning Framework via Federated Meta-Learning. 2020-01](https://arxiv.org/pdf/2001.03229.pdf)\n\n[(*) Think Locally, Act Globally: Federated Learning with Local and Global Representations. NeurIPS 2019 Workshop on Federated Learning distinguished student paper award](https://arxiv.org/pdf/2001.01523.pdf)\n\n[Federated Learning with Personalization Layers](https://arxiv.org/pdf/1912.00818.pdf)\n\n[Federated Adversarial Domain Adaptation](https://arxiv.org/pdf/1911.02054.pdf)\n\n[Federated Evaluation of On-device Personalization](https://arxiv.org/pdf/1910.10252.pdf)\n\n[Federated Learning with Unbiased Gradient Aggregation and Controllable Meta Updating](https://arxiv.org/pdf/1910.08234.pdf)\n\n[Overcoming Forgetting in Federated Learning on Non-IID Data](https://arxiv.org/pdf/1910.07796.pdf)\n\n[Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://arxiv.org/pdf/1910.01991.pdf)\n\n[Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data](https://arxiv.org/pdf/1903.02891.pdf)\n\n[Improving Federated Learning Personalization via Model Agnostic Meta Learning](https://arxiv.org/pdf/1909.12488.pdf)\n\n[Measure Contribution of Participants in Federated Learning](https://arxiv.org/pdf/1909.08525.pdf)\n\n[(*) Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://arxiv.org/pdf/1909.06335.pdf)\n\n[Multi-hop Federated Private Data Augmentation with Sample Compression](https://arxiv.org/pdf/1907.06426.pdf)\n\n[Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications](https://arxiv.org/pdf/1907.01132.pdf)\n\n[Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms](https://arxiv.org/pdf/1906.01736.pdf)\n\n[Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data](https://arxiv.org/pdf/1905.07210.pdf)\n\n[Robust and Communication-Efficient Federated Learning from Non-IID Data](https://arxiv.org/pdf/1903.02891.pdf)\n\n[High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions](https://arxiv.org/pdf/1902.08999.pdf)\n\n[Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://arxiv.org/pdf/1804.08333.pdf)\n\n[Federated Meta-Learning with Fast Convergence and Efficient Communication](https://arxiv.org/pdf/1802.07876.pdf)\n\n[Robust Federated Learning Through Representation Matching and Adaptive Hyper-parameters](https://arxiv.org/pdf/1912.13075.pdf)\n\n[Towards Efficient Scheduling of Federated Mobile Devices under Computational and Statistical Heterogeneity](https://arxiv.org/pdf/2005.12326.pdf)\n\n[Client Adaptation improves Federated Learning with Simulated Non-IID Clients](https://arxiv.org/pdf/2007.04806.pdf)\n\n[Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://arxiv.org/pdf/2007.07481.pdf)\n\n[Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity. ICDCS 2021.](https://arxiv.org/abs/2105.00562)\n\n\n## Vertical Federated Learning\n[SecureBoost: A Lossless Federated Learning Framework](https://arxiv.org/pdf/1901.08755.pdf)\n\n[Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator](https://arxiv.org/pdf/1911.09824.pdf)\n\n[A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression](https://arxiv.org/pdf/1912.00513.pdf)\n\n[Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption](https://arxiv.org/pdf/1711.10677.pdf)\n\n[Entity Resolution and Federated Learning get a Federated Resolution.](https://arxiv.org/pdf/1803.04035.pdf)\n\n[Multi-Participant Multi-Class Vertical Federated Learning](https://arxiv.org/pdf/2001.11154.pdf)\n\n[A Communication-Efficient Collaborative Learning Framework for Distributed Features](https://arxiv.org/pdf/1912.11187.pdf)\n\n[Asymmetrical Vertical Federated Learning](https://arxiv.org/pdf/2004.07427.pdf)\nResearcher: Tencent Cloud, Libin Wang\n\n[VAFL: a Method of Vertical Asynchronous Federated Learning, ICML workshop on FL, 2020](https://arxiv.org/abs/2007.06081)\n\n\n## Decentralized FL\n[Central Server Free Federated Learning over Single-sided Trust Social Networks](https://arxiv.org/pdf/1910.04956.pdf)\n\n[Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent](https://arxiv.org/pdf/1705.09056.pdf)\n\n[Multi-consensus Decentralized Accelerated Gradient Descent](https://arxiv.org/pdf/2005.00797.pdf)\n\n[Decentralized Bayesian Learning over Graphs. 2019-05](https://arxiv.org/pdf/1905.10466.pdf)\n\n[BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning](https://arxiv.org/pdf/1905.06731.pdf)\n\n[Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning](https://arxiv.org/pdf/1811.09904.pdf)\n\n[Matcha: Speeding Up Decentralized SGD via Matching Decomposition Sampling](https://arxiv.org/pdf/1905.09435.pdf)\n\n\n## Hierarchical FL\n[Client-Edge-Cloud Hierarchical Federated Learning](https://arxiv.org/pdf/1905.06641.pdf)\n\n[(FL startup: Tongdun, HangZhou, China) Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework. 2020-02](https://arxiv.org/pdf/2002.01647.pdf)\n\n[HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning](https://arxiv.org/pdf/2002.11343.pdf)\n\n[Hierarchical Federated Learning Across Heterogeneous Cellular Networks](https://arxiv.org/pdf/1909.02362.pdf)\n\n[Enhancing Privacy via Hierarchical Federated Learning](https://arxiv.org/pdf/2004.11361.pdf)\n\n[Federated learning with hierarchical clustering of local updates to improve training on non-IID data. 2020](https://arxiv.org/pdf/2004.11791.pdf)\n\n[Federated Hierarchical Hybrid Networks for Clickbait Detection](https://arxiv.org/pdf/1906.00638.pdf)\n\n[Matcha: Speeding Up Decentralized SGD via Matching Decomposition Sampling](https://arxiv.org/pdf/1905.09435.pdf) (in above section as well)\n\n## Neural Architecture Search\n[FedNAS: Federated Deep Learning via Neural Architecture Search. CVPR 2020. 2020-04-18](https://arxiv.org/pdf/2004.08546.pdf\n\n[Real-time Federated Evolutionary Neural Architecture Search. 2020-03](https://arxiv.org/pdf/2003.02793.pdf)\n\n[Federated Neural Architecture Search. 2020-06-14](https://arxiv.org/pdf/2002.06352.pdf)\n\n[Differentially-private Federated Neural Architecture Search. 2020-06](https://arxiv.org/pdf/2006.10559.pdf)\n\n## Transfer Learning\n\n[Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data](https://arxiv.org/pdf/1811.11479.pdf)\n\n[Secure Federated Transfer Learning. IEEE Intelligent Systems 2018.](https://arxiv.org/pdf/1812.03337.pdf)\n\n\n[FedMD: Heterogenous Federated Learning via Model Distillation](https://arxiv.org/pdf/1910.03581.pdf)\n\n[Secure and Efficient Federated Transfer Learning](https://arxiv.org/pdf/1910.13271.pdf)\n\n[Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data](https://arxiv.org/pdf/1907.02745.pdf)\n\n\n[Decentralized Differentially Private Segmentation with PATE. 2020-04](https://arxiv.org/pdf/2004.06567.pdf) \\\nHighlights: apply the ICLR 2017 paper \"Semisupervised knowledge transfer for deep learning from private training data\"\n\n[Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning. 2020](https://arxiv.org/pdf/2005.06105.pdf)\n\n[(FL startup: Tongdun, HangZhou, China) Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework. 2020-02](https://arxiv.org/pdf/2002.01647.pdf)\n\n[Cooperative Learning via Federated Distillation over Fading Channels](https://arxiv.org/pdf/2002.01337.pdf)\n\n\n[(*) Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer](https://arxiv.org/pdf/1912.11279.pdf)\n\n[Federated Reinforcement Distillation with Proxy Experience Memory](https://arxiv.org/pdf/1907.06536.pdf)\n\n## Continual Learning\n[Federated Continual Learning with Adaptive Parameter Communication. 2020-03](https://arxiv.org/pdf/2003.03196.pdf)\n\n## Semi-Supervised Learning\n[Federated Semi-Supervised Learning with Inter-Client Consistency. 2020](https://arxiv.org/pdf/2006.12097.pdf)\n\n[Semi-supervised knowledge transfer for deep learning from private training data. ICLR 2017](https://arxiv.org/pdf/1610.05755.pdf)\n\n[Scalable private learning with PATE. ICLR 2018. ](https://arxiv.org/pdf/1802.08908.pdf)\n\n\n## Domain Adaptation\n[Federated Adversarial Domain Adaptation. ICLR 2020.](https://arxiv.org/pdf/1911.02054.pdf)\n\n## Reinforcement Learning\n[Federated Deep Reinforcement Learning](https://arxiv.org/pdf/1901.08277.pdf)\n\n## Bayesian Learning \n[Differentially Private Federated Variational Inference. NeurIPS 2019 FL Workshop. 2019-11-24.](https://arxiv.org/pdf/1911.10563.pdf)\n\n## Causal Learning\n[Towards Causal Federated Learning For Enhanced Robustness and Privacy. ICLR 2021 DPML Workshop](https://arxiv.org/pdf/2104.06557.pdf)\n\n# Trustworthy AI: adversarial attack, privacy, fairness, incentive mechanism, etc.\n\n## Adversarial Attack and Defense\n[An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies. 2020-04-01](https://arxiv.org/pdf/2004.04676.pdf)\nCitation: 0\n\n[How To Backdoor Federated Learning. 2018-07-02. AISTATS 2020](https://arxiv.org/pdf/1807.00459.pdf)\nCitation: 128\n\n[Can You Really Backdoor Federated Learning?. NeruIPS 2019. 2019-11-18](https://arxiv.org/pdf/1911.07963.pdf)\nHighlight: by Google\nCitation: 9\n\n[DBA: Distributed Backdoor Attacks against Federated Learning. ICLR 2020.](https://openreview.net/pdf?id=rkgyS0VFvr)\nCitation: 66\n\n[CRFL: Certifiably Robust Federated Learning against Backdoor Attacks. ICML 2021.](https://arxiv.org/pdf/2106.08283.pdf)\n\n[Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning. ACM CCS 2017. 2017-02-14](https://arxiv.org/pdf/1702.07464.pdf)\nCitation: 284\n\n[Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://arxiv.org/pdf/1803.01498.pdf)\nCitation: 112\n\n[Deep Leakage from Gradients. NIPS 2019](https://papers.nips.cc/paper/9617-deep-leakage-from-gradients.pdf)\nCitation: 31\n\n[Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning. 2018-12-03](https://arxiv.org/pdf/1812.00910.pdf)\nCitation: 46\n\n[Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning. INFOCOM 2019](https://arxiv.org/pdf/1812.00535.pdf)\nCitation: 56\nHighlight: server-side attack\n\n[Analyzing Federated Learning through an Adversarial Lens. ICML 2019.](https://arxiv.org/pdf/1811.12470.pdf). \nCitation: 60\nHighlight: client attack\n\n[Mitigating Sybils in Federated Learning Poisoning. 2018-08-14. RAID 2020](https://arxiv.org/pdf/1808.04866.pdf)\nCitation: 41\nHighlight: defense\n\n[RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets, AAAI 2019](https://arxiv.org/abs/1811.03761)\nCitation: 34\n\n[(*) A Framework for Evaluating Gradient Leakage Attacks in Federated Learning. 2020-04-22](https://arxiv.org/pdf/2004.10397.pdf)\nResearcher: Wenqi Wei, Ling Liu, GaTech\n\n[(*) Local Model Poisoning Attacks to Byzantine-Robust Federated Learning. 2019-11-26](https://arxiv.org/pdf/1911.11815.pdf)\n\n[NeurIPS 2020 Submission: Backdoor Attacks on Federated Meta-Learning](https://arxiv.org/pdf/2006.07026.pdf)\nResearcher: Chien-Lun Chen, USC\n\n[Towards Realistic Byzantine-Robust Federated Learning. 2020-04-10](https://arxiv.org/pdf/2004.04986.pdf)\n\n[Data Poisoning Attacks on Federated Machine Learning. 2020-04-19](https://arxiv.org/pdf/2004.10020.pdf)\n\n[Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning. 2020-04-27](https://arxiv.org/pdf/2004.12571.pdf)\n\n[Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data. 2020-06-22](https://arxiv.org/pdf/2006.13041.pdf)\nResearcher: Suhas Diggavi, UCLA (https://scholar.google.com/citations?hl=en\u0026user=hjTzNuQAAAAJ\u0026view_op=list_works\u0026sortby=pubdate)\n\n[(*) NeurIPS 2020 submission: FedMGDA+: Federated Learning meets Multi-objective Optimization. 2020-06-20](https://arxiv.org/pdf/2006.11489.pdf)\n\n[(*) NeurIPS 2020 submission: Free-rider Attacks on Model Aggregation in Federated Learning. 2020-06-26](https://arxiv.org/pdf/2006.11901.pdf)\n\n[FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications. 2020-06-28](https://arxiv.org/pdf/2006.15632.pdf)\n\n\n[Privacy-preserving Weighted Federated Learning within Oracle-Aided MPC Framework. 2020-05-17](https://arxiv.org/pdf/2003.07630.pdf)\nCitation: 0\n\n[BASGD: Buffered Asynchronous SGD for Byzantine Learning. 2020-03-02](https://arxiv.org/pdf/2003.00937.pdf)\n\n[Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees. 2020-02-25](https://arxiv.org/pdf/2002.10940.pdf)\nCitation: 1\n\n[Learning to Detect Malicious Clients for Robust Federated Learning. 2020-02-01](https://arxiv.org/pdf/2002.00211.pdf)\n\n[Robust Aggregation for Federated Learning. 2019-12-31](https://arxiv.org/pdf/1912.13445.pdf)\nCitation: 9\n\n[Towards Deep Federated Defenses Against Malware in Cloud Ecosystems. 2019-12-27](https://arxiv.org/pdf/1912.12370.pdf)\n\n[Attack-Resistant Federated Learning with Residual-based Reweighting. 2019-12-23](https://arxiv.org/pdf/1912.11464.pdf)\n\n[Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer. 2019-12-24](https://arxiv.org/pdf/1912.11279.pdf)\nCitation: 1\n\n[Free-riders in Federated Learning: Attacks and Defenses. 2019-11-28](https://arxiv.org/pdf/1911.12560.pdf)\n\n[Robust Federated Learning with Noisy Communication. 2019-11-01](https://arxiv.org/pdf/1911.00251.pdf)\nCitation: 4\n\n[Abnormal Client Behavior Detection in Federated Learning. 2019-10-22](https://arxiv.org/pdf/1910.09933.pdf)\nCitation: 3\n\n[Eavesdrop the Composition Proportion of Training Labels in Federated Learning. 2019-10-14](https://arxiv.org/pdf/1910.06044.pdf)\nCitation: 0\n\n[Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging. 2019-09-11](https://arxiv.org/pdf/1909.05125.pdf)\n\n[An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learning. 2019-08-22](https://arxiv.org/pdf/1908.08340.pdf)\n\n[Secure Distributed On-Device Learning Networks With Byzantine Adversaries. 2019-06-03](https://arxiv.org/pdf/1906.00887.pdf)\nCitation: 3\n\n[Robust Federated Training via Collaborative Machine Teaching using Trusted Instances. 2019-05-03](https://arxiv.org/pdf/1905.02941.pdf)\nCitation: 2\n\n[Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting. 2018-11-23](https://arxiv.org/pdf/1811.09712.pdf)\nCitation: 4\n\n[Inverting Gradients - How easy is it to break privacy in federated learning? 2020-03-31](https://arxiv.org/pdf/2003.14053.pdf)\nCitation: 3\n\n[Quantification of the Leakage in Federated Learning. 2019-10-12](https://arxiv.org/pdf/1910.05467.pdf)\nCitation: 1\n\n## Privacy\n[Practical Secure Aggregation for Federated Learning on User-Held Data. NIPS 2016 workshop](https://arxiv.org/pdf/1611.04482.pdf)\nHighlight: cryptology\n\n[Differentially Private Federated Learning: A Client Level Perspective. NIPS 2017 Workshop](https://arxiv.org/pdf/1712.07557.pdf)\n\n[Exploiting Unintended Feature Leakage in Collaborative Learning. S\u0026P 2019. 2018-05-10](https://arxiv.org/pdf/1805.04049.pdf)\nCitation: 105\n\n[(x) Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning. 2018-05](https://arxiv.org/pdf/1805.05838.pdf)\n\n[A Hybrid Approach to Privacy-Preserving Federated Learning. AISec 2019. 2018-12-07](https://arxiv.org/pdf/1812.03224.pdf)\nCitation: 35\n\n[A generic framework for privacy preserving deep learning. PPML 2018. 2018-11-09](https://arxiv.org/pdf/1811.04017.pdf)\nCitation: 36\n\n[Federated Generative Privacy. IJCAI 2019 FL workshop. 2019-10-08](https://arxiv.org/pdf/1910.08385.pdf)\nCitation: 4\n\n[Enhancing the Privacy of Federated Learning with Sketching. 2019-11-05](https://arxiv.org/pdf/1911.01812.pdf)\nCitaiton: 0\n\n[Federated Learning with Bayesian Differential Privacy. 2019-11-22](https://arxiv.org/pdf/1911.10071.pdf)\nCitation: 5\n\nHybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning. AISec 2019. 2019-12-12\n[https://aisec.cc/](https://arxiv.org/pdf/1912.05897.pdf)\n\n[Private Federated Learning with Domain Adaptation. NeurIPS 2019 FL workshop. 2019-12-13](https://arxiv.org/pdf/1912.06733.pdf)\n\n[iDLG: Improved Deep Leakage from Gradients. 2020-01-08](https://arxiv.org/pdf/2001.02610.pdf)\nCitation: 3\n\n[Anonymizing Data for Privacy-Preserving Federated Learning. 2020-02-21](https://arxiv.org/pdf/2002.09096.pdf)\n\n[Practical and Bilateral Privacy-preserving Federated Learning. 2020-02-23](https://arxiv.org/pdf/2002.09843.pdf)\nCitation: 0\n\n[Decentralized Policy-Based Private Analytics. 2020-03-14](https://arxiv.org/pdf/2003.06612.pdf)\nCitation: 0\n\n[FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection. DASFAA 2020. 2020-03-24](https://arxiv.org/pdf/2003.10637.pdf)\nCitation: 0\n\n[Learn to Forget: User-Level Memorization Elimination in Federated Learning. 2020-03-24](https://arxiv.org/pdf/2003.10933.pdf)\n\n[LDP-Fed: Federated Learning with Local Differential Privacy. EdgeSys 2020. 2020-04-01](https://arxiv.org/pdf/2006.03637.pdf)\nResearcher: Ling Liu, GaTech\nCitation: 1\n\n[PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networks. 2020-04-05](https://arxiv.org/pdf/2004.02264.pdf)\nCitation: 0\n\n[Local Differential Privacy based Federated Learning for Internet of Things. 2020-04-09](https://arxiv.org/pdf/2004.08856.pdf)\nCitation: 0\n\n[Differentially Private AirComp Federated Learning with Power Adaptation Harnessing Receiver Noise. 2020-04.](https://arxiv.org/pdf/2004.06337.pdf)\n\n[Decentralized Differentially Private Segmentation with PATE. MICCAI 2020 Under Review. 2020-04](https://arxiv.org/pdf/2004.06567.pdf) \\\nHighlights: apply the ICLR 2017 paper \"Semisupervised knowledge transfer for deep learning from private training data\"\n\n\n[Enhancing Privacy via Hierarchical Federated Learning. 2020-04-23](https://arxiv.org/pdf/2004.11361.pdf)\n\n[Privacy Preserving Distributed Machine Learning with Federated Learning. 2020-04-25](https://arxiv.org/pdf/2004.12108.pdf)\nCitation: 0\n\n[Exploring Private Federated Learning with Laplacian Smoothing. 2020-05-01](https://arxiv.org/pdf/2005.00218.pdf)\nCitation: 0\n\n[Information-Theoretic Bounds on the Generalization Error and Privacy Leakage in Federated Learning. 2020-05-05](https://arxiv.org/pdf/2005.02503.pdf)\nCitation: 0\n\n[Efficient Privacy Preserving Edge Computing Framework for Image Classification. 2020-05-10](https://arxiv.org/pdf/2005.04563.pdf)\nCitation: 0\n\n[A Distributed Trust Framework for Privacy-Preserving Machine Learning. 2020-06-03](https://arxiv.org/pdf/2006.02456.pdf)\nCitation: 0\n\n[Secure Byzantine-Robust Machine Learning. 2020-06-08](https://arxiv.org/pdf/2006.04747.pdf)\n\n[ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing. 2020-06-08](https://arxiv.org/pdf/2006.04593.pdf)\n\n[Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Control. 2020-06-09](https://arxiv.org/pdf/2006.05459.pdf)\nCitation: 0\n\n[(*) Distributed Differentially Private Averaging with Improved Utility and Robustness to Malicious Parties. 2020-06-12](https://arxiv.org/pdf/2006.07218.pdf)\nCitation: 0\n\n[GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators. 2020-06-15](https://arxiv.org/pdf/2006.08848.pdf)\nCitation: 0\n\n[Federated Learning with Differential Privacy:Algorithms and Performance Analysis](https://arxiv.org/pdf/1911.00222.pdf)\nCitation: 2\n\n## Fairness\n[Fair Resource Allocation in Federated Learning. ICLR 2020.](https://arxiv.org/pdf/1905.10497.pdf)\n\n[Hierarchically Fair Federated Learning](https://arxiv.org/pdf/2004.10386.pdf)\n\n[Towards Fair and Privacy-Preserving Federated Deep Models](https://arxiv.org/pdf/1906.01167.pdf)\n\n## Interpretability\n[Interpret Federated Learning with Shapley Values. ](https://arxiv.org/pdf/1905.04519.pdf)\n\n\n## Incentive Mechanism\n\n[Record and reward federated learning contributions with blockchain. IEEE CyberC 2019](https://mblocklab.com/RecordandReward.pdf)\n\n[FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC. ICDCS 2020](https://arxiv.org/pdf/2002.09699.pdf)\n\n[Toward an Automated Auction Framework for Wireless Federated Learning Services Market](https://arxiv.org/pdf/1912.06370.pdf)\n\n[Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism](https://arxiv.org/pdf/1911.05642.pdf)\n\n[Motivating Workers in Federated Learning: A Stackelberg Game Perspective](https://arxiv.org/pdf/1908.03092.pdf)\n\n[Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach](https://arxiv.org/pdf/1905.07479.pdf)\n\n[A Learning-based Incentive Mechanism forFederated Learning](https://www.u-aizu.ac.jp/~pengli/files/fl_incentive_iot.pdf)\n\n[A Crowdsourcing Framework for On-Device Federated Learning](https://arxiv.org/pdf/1911.01046.pdf)\n\n# System Challenges: communication and computational resource constrained, software and hardware heterogeneity, and FL wireless communication system\n\n## Communication Efficiency\n[Federated Learning: Strategies for Improving Communication Efficiency](https://arxiv.org/pdf/1610.05492.pdf)\nHighlights: optimization\n\n[Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training. ICLR 2018. 2017-12-05](https://arxiv.org/pdf/1712.01887.pdf)\nHighlights: gradient compression\nCitation: 298\n\n[NeurIPS 2020 submission: Artemis: tight convergence guarantees for bidirectional compression in Federated Learning. 2020-06-25](https://arxiv.org/pdf/2006.14591.pdf)\nHighlights: bidirectional gradient compression\n\n[Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MEC. 2020-06-21](https://arxiv.org/pdf/2006.13044.pdf)\n\n[(x) Federated Mutual Learning. 2020-06-27](https://arxiv.org/pdf/2006.16765.pdf)\nHighlights: Duplicate to Deep Mutual Learning. CVPR 2018\n\n[A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning. 2020-06-19](https://arxiv.org/pdf/2006.11077.pdf)\nResearcher: Peter Richtárik\n\n[Federated Learning With Quantized Global Model Updates. 2020-06-18](https://arxiv.org/pdf/2006.10672.pdf)\nResearcher: Mohammad Mohammadi Amiri, Princeton, Information Theory and Machine Learning\nHighlights: model compression\n\n[Federated Learning with Compression: Unified Analysis and Sharp Guarantees. 2020-07-02](https://arxiv.org/pdf/2007.01154.pdf)\nHighlight: non-IID, gradient compression + local SGD\nResearcher: Mehrdad Mahdavi, Jin Rong’s PhD http://www.cse.psu.edu/~mzm616/\n\n[Evaluating the Communication Efficiency in Federated Learning Algorithm. 2020-04-06](https://arxiv.org/pdf/2004.02738.pdf)\n\n[Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning. 2020-05-21](https://arxiv.org/pdf/2003.09603.pdf)\n\n[Ternary Compression for Communication-Efficient Federated Learning. 2020-05-07](https://arxiv.org/pdf/2003.03564.pdf)\n\n[Gradient Statistics Aware Power Control for Over-the-Air Federated Learning. 2020-05-04](https://arxiv.org/pdf/2003.02089.pdf)\n\n[Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selection. 2020-02-22](https://arxiv.org/pdf/2002.09692.pdf)\n\n[(*) RPN: A Residual Pooling Network for Efficient Federated Learning. ECAI 2020.](https://arxiv.org/pdf/2001.08600.pdf)\n\n[Intermittent Pulling with Local Compensation for Communication-Efficient Federated Learning. 2020-01-22](https://arxiv.org/pdf/2001.08277.pdf)\n\n[Hyper-Sphere Quantization: Communication-Efficient SGD for Federated Learning. 2019-11-12](https://arxiv.org/pdf/1911.04655.pdf)\n\n[L-FGADMM: Layer-Wise Federated Group ADMM for Communication Efficient Decentralized Deep Learning](https://arxiv.org/pdf/1911.03654.pdf)\n\n[Gradient Sparification for Asynchronous Distributed Training. 2019-10-24](https://arxiv.org/pdf/1910.10929.pdf)\n\n[High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning](https://arxiv.org/pdf/1910.03865.pdf)\n\n[SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead](https://arxiv.org/pdf/1910.01355.pdf)\n\n[Detailed comparison of communication efficiency of split learning and federated learning](https://arxiv.org/pdf/1909.09145.pdf)\n\n[Decentralized Federated Learning: A Segmented Gossip Approach](https://arxiv.org/pdf/1908.07782.pdf)\n\n[Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation](https://arxiv.org/pdf/1903.07424.pdf)\n\n[One-Shot Federated Learning](https://arxiv.org/pdf/1902.11175.pdf)\n\n[Multi-objective Evolutionary Federated Learning](https://arxiv.org/pdf/1812.07478.pdf)\n\n[Expanding the Reach of Federated Learning by Reducing Client Resource Requirements](https://arxiv.org/pdf/1812.07210.pdf)\n\n[Partitioned Variational Inference: A unified framework encompassing federated and continual learning](https://arxiv.org/pdf/1811.11206.pdf)\n\n[FedOpt: Towards communication efficiency and privacy preservation in federated learning](https://res.mdpi.com/d_attachment/applsci/applsci-10-02864/article_deploy/applsci-10-02864.pdf)\n\n[A performance evaluation of federated learning algorithms](https://www.researchgate.net/profile/Gregor_Ulm/[publication/329106719_A_Performance_Evaluation_of_Federated_Learning_Algorithms]/(links/5c0fabcfa6fdcc494febf907/A-Performance-Evaluation-of-Federated-Learning-Algorithms.pdf))\n\n\n\n## Straggler Problem\n\n[Coded Federated Learning. Presented at the Wireless Edge Intelligence Workshop, IEEE GLOBECOM 2019](https://arxiv.org/pdf/2002.09574.pdf)\n\n[Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning](https://arxiv.org/pdf/2002.04156.pdf)\n\n[Coded Federated Computing in Wireless Networks with Straggling Devices and Imperfect CSI](https://arxiv.org/pdf/1901.05239.pdf)\n\n[Information-Theoretic Perspective of Federated Learning](https://arxiv.org/pdf/1911.07652.pdf)\n\n\n## Computation Efficiency\n[NeurIPS 2020 Submission: Distributed Learning on Heterogeneous Resource-Constrained Devices](https://arxiv.org/pdf/2006.05403.pdf)\n\n[SplitFed: When Federated Learning Meets Split Learning](https://arxiv.org/pdf/2004.12088.pdf)\n\n[Lottery Hypothesis based Unsupervised Pre-training for Model Compression in Federated Learning](https://arxiv.org/pdf/2004.09817.pdf)\n\n[Secure Federated Learning in 5G Mobile Networks. 2020/04](https://arxiv.org/pdf/2004.06700.pdf) \n\n[ELFISH: Resource-Aware Federated Learning on Heterogeneous Edge Devices](https://arxiv.org/pdf/1912.01684.pdf)\n\n[Asynchronous Online Federated Learning for Edge Devices](https://arxiv.org/pdf/1911.02134.pdf)\n\n[(*) Secure Federated Submodel Learning](https://arxiv.org/pdf/1911.02254.pdf)\n\n[Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligence](https://arxiv.org/pdf/1910.09594.pdf)\n\n[Model Pruning Enables Efficient Federated Learning on Edge Devices](https://arxiv.org/pdf/1909.12326.pdf)\n\n[Towards Effective Device-Aware Federated Learning](https://arxiv.org/pdf/1908.07420.pdf)\n\n[Accelerating DNN Training in Wireless Federated Edge Learning System](https://arxiv.org/pdf/1905.09712.pdf)\n\n[Split learning for health: Distributed deep learning without sharing raw patient data](https://arxiv.org/pdf/1812.00564.pdf)\n\n[SmartPC: Hierarchical pace control in real-time federated learning system](https://www.ece.ucf.edu/~zsguo/pubs/conference_workshop/RTSS2019b.pdf)\n\n[DeCaf: Iterative collaborative processing over the edge](https://www.usenix.org/system/files/hotedge19-paper-kumar.pdf)\n\n## Wireless Communication and Cloud Computing\nResearcher: \nH. Vincent Poor\nhttps://ee.princeton.edu/people/h-vincent-poor\n\nHao Ye\nhttps://scholar.google.ca/citations?user=ok7OWEAAAAAJ\u0026hl=en\n\nYe Li\nhttp://liye.ece.gatech.edu/\n\n[Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup](https://arxiv.org/pdf/2006.09801.pdf)\nResearcher: Mehdi Bennis, Seong-Lyun Kim\n\n[Wireless Communications for Collaborative Federated Learning in the Internet of Things](https://arxiv.org/pdf/2006.02499.pdf)\n\n[Democratizing the Edge: A Pervasive Edge Computing Framework](https://arxiv.org/pdf/2007.00641.pdf)\n\n[UVeQFed: Universal Vector Quantization for Federated Learning](https://arxiv.org/pdf/2006.03262.pdf)\n\n[Federated Deep Learning Framework For Hybrid Beamforming in mm-Wave Massive MIMO](https://arxiv.org/pdf/2005.09969.pdf)\n\n[Efficient Federated Learning over Multiple Access Channel with Differential Privacy Constraints](https://arxiv.org/pdf/2005.07776.pdf)\n\n[A Secure Federated Learning Framework for 5G Networks](https://arxiv.org/pdf/2005.05752.pdf)\n\n[Federated Learning and Wireless Communications](https://arxiv.org/pdf/2005.05265.pdf)\n\n[Lightwave Power Transfer for Federated Learning-based Wireless Networks](https://arxiv.org/pdf/2005.03977.pdf)\n\n[Towards Ubiquitous AI in 6G with Federated Learning](https://arxiv.org/pdf/2004.13563.pdf)\n\n[Optimizing Over-the-Air Computation in IRS-Aided C-RAN Systems](https://arxiv.org/pdf/2004.09168.pdf)\n\n[Network-Aware Optimization of Distributed Learning for Fog Computing](https://arxiv.org/pdf/2004.08488.pdf)\n\n[On the Design of Communication Efficient Federated Learning over Wireless Networks](https://arxiv.org/pdf/2004.07351.pdf)\n\n[Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface](https://arxiv.org/pdf/2004.05843.pdf)\n\n[Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective](https://arxiv.org/pdf/2004.04314.pdf)\n\n[Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach](https://arxiv.org/pdf/2004.04104.pdf)\n\n[A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus](https://arxiv.org/pdf/2004.00773.pdf)\n\n[Scheduling for Cellular Federated Edge Learning with Importance and Channel. 2020-04](https://arxiv.org/pdf/2004.00490.pdf)\n\n[Differentially Private Federated Learning for Resource-Constrained Internet of Things. 2020-03](https://arxiv.org/pdf/2003.12705.pdf)\n\n[Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networks. 2020-03](https://arxiv.org/pdf/2003.09375.pdf)\n\n[Gradient Estimation for Federated Learning over Massive MIMO Communication Systems](https://arxiv.org/pdf/2003.08059.pdf)\n\n[Adaptive Federated Learning With Gradient Compression in Uplink NOMA](https://arxiv.org/pdf/2003.01344.pdf)\n\n[Performance Analysis and Optimization in Privacy-Preserving Federated Learning](https://arxiv.org/pdf/2003.00229.pdf)\n\n[Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design](https://arxiv.org/pdf/2003.00199.pdf)\n\n[Federated Over-the-Air Subspace Learning and Tracking from Incomplete Data](https://arxiv.org/pdf/2002.12873.pdf)\n\n[Decentralized Federated Learning via SGD over Wireless D2D Networks](https://arxiv.org/pdf/2002.12507.pdf)\n\n[HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning](https://arxiv.org/pdf/2002.11343.pdf)\n\n[Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms](https://arxiv.org/pdf/2002.08196.pdf)\n\n[Wireless Federated Learning with Local Differential Privacy](https://arxiv.org/pdf/2002.05151.pdf)\n\n[Cooperative Learning via Federated Distillation over Fading Channels](https://arxiv.org/pdf/2002.01337.pdf)\n\n[Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocation. 2020-02](https://arxiv.org/pdf/2002.01337.pdf)\n\n[Learning from Peers at the Wireless Edge](https://arxiv.org/pdf/2001.11567.pdf)\n\n[Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge](https://arxiv.org/pdf/2001.10402.pdf)\n\n[Communication Efficient Federated Learning over Multiple Access Channels](https://arxiv.org/pdf/2001.08737.pdf)\n\n[Convergence Time Optimization for Federated Learning over Wireless Networks](https://arxiv.org/pdf/2001.07845.pdf)\n\n[One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis](https://arxiv.org/pdf/2001.05713.pdf)\n\n[Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks. IEEE Internet of Things Journal. 2020](https://arxiv.org/pdf/1912.13163.pdf)\n\n[Asynchronous Federated Learning with Differential Privacy for Edge Intelligence](https://arxiv.org/pdf/1912.07902.pdf)\n\n[Federated learning with multichannel ALOHA](https://arxiv.org/pdf/1912.06273.pdf)\n\n[Federated Learning with Autotuned Communication-Efficient Secure Aggregation](https://arxiv.org/pdf/1912.00131.pdf)\n\n[Bandwidth Slicing to Boost Federated Learning in Edge Computing](https://arxiv.org/pdf/1911.07615.pdf)\n\n[Energy Efficient Federated Learning Over Wireless Communication Networks](https://arxiv.org/pdf/1911.02417.pdf)\n\n[Device Scheduling with Fast Convergence for Wireless Federated Learning](https://arxiv.org/pdf/1911.00856.pdf)\n\n[Energy-Aware Analog Aggregation for Federated Learning with Redundant Data](https://arxiv.org/pdf/1911.00188.pdf)\n\n[Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks](https://arxiv.org/pdf/1910.14648.pdf)\n\n[Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation](https://arxiv.org/pdf/1910.13067.pdf)\n\n[Federated Learning over Wireless Networks: Optimization Model Design and Analysis](http://networking.khu.ac.kr/layouts/net/publications/data/2019\\)Federated%20Learning%20over%20Wireless%20Network.pdf)\n\n[Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approach](https://arxiv.org/pdf/1910.09172.pdf)\n\n[Reliable Federated Learning for Mobile Networks](https://arxiv.org/pdf/1910.06837.pdf)\n\n[FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization](https://arxiv.org/pdf/1909.13014.pdf)\n\n[Active Federated Learning](https://arxiv.org/pdf/1909.12641.pdf)\n\n[Cell-Free Massive MIMO for Wireless Federated Learning](https://arxiv.org/pdf/1909.12567.pdf)\n\n[A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://arxiv.org/pdf/1909.07972.pdf)\n\n[On Safeguarding Privacy and Security in the Framework of Federated Learning](https://arxiv.org/pdf/1909.06512.pdf)\n\n[On Safeguarding Privacy and Security in the Framework of Federated Learning](https://arxiv.org/pdf/1909.06512.pdf)\n\n[Hierarchical Federated Learning Across Heterogeneous Cellular Networks](https://arxiv.org/pdf/1909.02362.pdf)\n\n[Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges](https://arxiv.org/pdf/1908.06847.pdf)\n\n[Scheduling Policies for Federated Learning in Wireless Networks](https://arxiv.org/pdf/1908.06287.pdf)\n\n[Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costs](https://arxiv.org/pdf/1908.05891.pdf)\n\n[Federated Learning over Wireless Fading Channels](https://arxiv.org/pdf/1907.09769.pdf)\n\n[Energy-Efficient Radio Resource Allocation for Federated Edge Learning](https://arxiv.org/pdf/1907.06040.pdf)\n\n[Mobile Edge Computing, Blockchain and Reputation-based Crowdsourcing IoT Federated Learning: A Secure, Decentralized and Privacy-preserving System](https://arxiv.org/pdf/1906.10893.pdf)\n\n[Active Learning Solution on Distributed Edge Computing](https://arxiv.org/pdf/1906.10718.pdf)\n\n[Fast Uplink Grant for NOMA: a Federated Learning based Approach](https://arxiv.org/pdf/1905.04519.pdf)\n\n[Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air](https://arxiv.org/pdf/1901.00844.pdf)\n\n[Federated Learning via Over-the-Air Computation](https://arxiv.org/pdf/1812.11750.pdf)\n\n[Broadband Analog Aggregation for Low-Latency Federated Edge Learning](https://arxiv.org/pdf/1812.11494.pdf)\n\n[Federated Echo State Learning for Minimizing Breaks in Presence in Wireless Virtual Reality Networks](https://arxiv.org/pdf/1812.01202.pdf)\n\n[Joint Service Pricing and Cooperative Relay Communication for Federated Learning](https://arxiv.org/pdf/1811.12082.pdf)\n\n[In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning](https://arxiv.org/pdf/1809.07857.pdf)\n\n[Asynchronous Task Allocation for Federated and Parallelized Mobile Edge Learning](https://arxiv.org/pdf/1905.01656.pdf)\n\n[CoLearn: enabling federated learning in MUD-compliant IoT edge networks](CoLearn: enabling federated learning in MUD-compliant IoT edge networks)\n\n## FL System Design\n[Towards Federated Learning at Scale: System Design](https://arxiv.org/pdf/1902.01046.pdf)\n\n[FedML: A Research Library and Benchmark for Federated Machine Learning](https://arxiv.org/pdf/2007.13518.pdf)\n\n[A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://arxiv.org/pdf/1907.09693.pdf)\n\n[FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction](https://arxiv.org/pdf/2006.07273.pdf)\nResearcher: Georgios Damaskinos, MLSys, https://people.epfl.ch/georgios.damaskinos?lang=en\n\n[Heterogeneity-Aware Federated Learning](https://arxiv.org/pdf/2006.06983.pdf)\nResearcher: Mengwei Xu, PKU\n\nResponsive Web User Interface to Recover Training Data from User Gradients in Federated Learning\nhttps://ldp-machine-learning.herokuapp.com/\n\n[Decentralised Learning from Independent Multi-Domain Labels for Person Re-Identification](https://arxiv.org/pdf/2006.04150.pdf)\n\n[[startup] Industrial Federated Learning -- Requirements and System Design](https://arxiv.org/pdf/2005.06850.pdf)\n\n[(startup) Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy](https://arxiv.org/pdf/2007.00914.pdf)\n\n[(FL startup: Tongdun, HangZhou, China) Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework. 2020-02](https://arxiv.org/pdf/2002.01647.pdf)\n\n[(*) TiFL: A Tier-based Federated Learning System. HPDC 2020 (High-Performance Parallel and Distributed Computing).](https://arxiv.org/pdf/2001.09249.pdf)\n\n[FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC. ICDCS 2020 (2020 International Conference on Distributed Computing Systems)](https://arxiv.org/pdf/2002.09699.pdf)\n\n[Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach. ICDCS 2020 (2020 International Conference on Distributed Computing Systems)](https://arxiv.org/pdf/2001.04756.pdf)\n\n[Quantifying the Performance of Federated Transfer Learning](https://arxiv.org/pdf/1912.12795.pdf)\n\n[ELFISH: Resource-Aware Federated Learning on Heterogeneous Edge Devices](https://arxiv.org/pdf/1912.01684.pdf)\n\n[Privacy is What We Care About: Experimental Investigation of Federated Learning on Edge Devices](https://arxiv.org/pdf/1911.04559.pdf)\n\n[Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning](https://arxiv.org/pdf/1910.11567.pdf)\n\n[BAFFLE : Blockchain Based Aggregator Free Federated Learning](https://arxiv.org/pdf/1909.07452.pdf)\n\n[Edge AIBench: Towards Comprehensive End-to-end Edge Computing Benchmarking](https://arxiv.org/pdf/1908.01924.pdf)\n\n[Functional Federated Learning in Erlang (ffl-erl)](https://arxiv.org/pdf/1808.08143.pdf)\n\n[HierTrain: Fast Hierarchical Edge AI Learning With Hybrid Parallelism in Mobile-Edge-Cloud Computing](https://arxiv.org/pdf/2003.09876.pdf)\n\n\n# Models and Applications\n\n## Models\n### Graph Neural Networks\n\n[Peer-to-peer federated learning on graphs](https://arxiv.org/pdf/1901.11173)\n\n[Towards Federated Graph Learning for Collaborative Financial Crimes Detection](https://arxiv.org/pdf/1909.12946)\n\n[A Graph Federated Architecture with Privacy Preserving Learning](https://arxiv.org/pdf/2104.13215)\n\n[Federated Myopic Community Detection with One-shot Communication](https://arxiv.org/pdf/2106.07255)\n\n[Federated Dynamic GNN with Secure Aggregation](https://arxiv.org/pdf/2009.07351)\n\n[Privacy-Preserving Graph Neural Network for Node Classification](https://arxiv.org/pdf/2005.11903)\n\n[ASFGNN: Automated Separated-Federated Graph Neural Network](https://arxiv.org/pdf/2011.03248)\n\n[GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs](https://arxiv.org/pdf/2012.04187)\n\n[FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation](https://arxiv.org/pdf/2102.04925)\n\n[FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks](https://arxiv.org/pdf/2104.07145) \n\n[FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search](https://arxiv.org/pdf/2104.04141)\n\n[Cluster-driven Graph Federated Learning over Multiple Domains](https://arxiv.org/pdf/2104.14628)\n\n[FedGL: Federated Graph Learning Framework with Global Self-Supervision](https://arxiv.org/pdf/2105.03170)\n\n[Federated Graph Learning -- A Position Paper](https://arxiv.org/pdf/2105.11099)\n\n[SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks](https://arxiv.org/pdf/2106.02743)\n\n[Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling](https://arxiv.org/pdf/2106.05223)\n\n[A Vertical Federated Learning Framework for Graph Convolutional Network](https://arxiv.org/pdf/2106.11593)\n\n[Federated Graph Classification over Non-IID Graphs](https://arxiv.org/pdf/2106.13423)\n\n[Subgraph Federated Learning with Missing Neighbor Generation](https://arxiv.org/pdf/2106.13430)\n\n### Federated Learning on Knowledge Graphs\n\n[FedE: Embedding Knowledge Graphs in Federated Setting](https://arxiv.org/pdf/2010.12882)\n\n[Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty](https://arxiv.org/pdf/2011.11369)\n\n[Federated Knowledge Graphs Embedding](https://arxiv.org/pdf/2105.07615)\n\n\n### Generative Models (GAN, Bayesian Generative Models, etc)\n\n[Discrete-Time Cox Models](https://arxiv.org/pdf/2006.08997.pdf)\n\n[Generative Models for Effective ML on Private, Decentralized Datasets. Google. ICLR 2020](https://arxiv.org/pdf/1911.06679.pdf)\nCitation: 8\n\n[MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets. 2018-11-09](https://arxiv.org/pdf/1811.03850.pdf)\n\n[(GAN) Federated Generative Adversarial Learning. 2020-05-07](https://arxiv.org/pdf/2005.03793.pdf)\nCitation: 0\n\n[Differentially Private Data Generative Models](https://arxiv.org/pdf/1812.02274.pdf)\n\n[GRAFFL: Gradient-free Federated Learning of a Bayesian Generative Model](https://arxiv.org/pdf/1910.08489.pdf)\n\n### VAE (Variational Autoencoder)\n\n[(VAE) An On-Device Federated Learning Approach for Cooperative Anomaly Detection](https://arxiv.org/pdf/2002.12301.pdf)\n\n### MF (Matrix Factorization)\n\n[Secure Federated Matrix Factorization](https://arxiv.org/pdf/1906.05108.pdf)\n\n[(Clustering) Federated Clustering via Matrix Factorization Models: From Model Averaging to Gradient Sharing](https://arxiv.org/pdf/2002.04930.pdf)\n\n[Privacy Threats Against Federated Matrix Factorization](https://arxiv.org/pdf/2007.01587.pdf)\n\n### GBDT (Gradient Boosting Decision Trees)\n\n[Practical Federated Gradient Boosting Decision Trees. AAAI 2020.](https://arxiv.org/pdf/1911.04206.pdf)\n\n[Federated Extra-Trees with Privacy Preserving](https://arxiv.org/pdf/2002.07323.pdf)\n\n[SecureGBM: Secure Multi-Party Gradient Boosting](https://arxiv.org/pdf/1911.11997.pdf)\n\n[Federated Forest](https://arxiv.org/pdf/1905.10053.pdf)\n\n[The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost](https://arxiv.org/pdf/1907.07157.pdf)\n\n### Other Model\n[Privacy Preserving QoE Modeling using Collaborative Learning](https://arxiv.org/pdf/1906.09248.pdf)\n\n\n[Distributed Dual Coordinate Ascent in General Tree Networks and Its Application in Federated Learning](https://arxiv.org/pdf/1703.04785.pdf)\n\n## Natural language Processing\n[Federated pretraining and fine tuning of BERT using clinical notes from multiple silos](https://arxiv.org/pdf/2002.08562.pdf)\n\n[Federated Learning for Mobile Keyboard Prediction](https://arxiv.org/pdf/1811.03604.pdf)\n\n[Federated Learning for Keyword Spotting](https://arxiv.org/pdf/1810.05512.pdf)\n\n[generative sequence models (e.g., language models)](https://arxiv.org/pdf/2006.07490.pdf)\n\n[Pretraining Federated Text Models for Next Word Prediction](https://arxiv.org/pdf/2005.04828.pdf)\n\n[FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning. MSRA. 2020-03.](https://arxiv.org/pdf/2003.09288.pdf)\n\n[Federated Learning of N-gram Language Models. Google. ACL 2019.](https://www.aclweb.org/anthology/K19-1012.pdf)\n\n[Federated User Representation Learning](https://arxiv.org/pdf/1909.12535.pdf)\n\n[Two-stage Federated Phenotyping and Patient Representation Learning](https://arxiv.org/pdf/1908.05596.pdf)\n\n[Federated Learning for Emoji Prediction in a Mobile Keyboard](https://arxiv.org/pdf/1906.04329.pdf)\n\n[Federated AI lets a team imagine together: Federated Learning of GANs](https://arxiv.org/pdf/1906.03595.pdf)\n\n[Federated Learning Of Out-Of-Vocabulary Words](https://arxiv.org/pdf/1903.10635.pdf)\n\n[Learning Private Neural Language Modeling with Attentive Aggregation](https://arxiv.org/pdf/1812.07108.pdf)\n\n[Applied Federated Learning: Improving Google Keyboard Query Suggestions](https://arxiv.org/pdf/1812.02903.pdf)\n\n[Federated Learning for Ranking Browser History Suggestions](https://arxiv.org/pdf/1911.11807.pdf)\n\n## Computer Vision\n[Federated Face Anti-spoofing](https://arxiv.org/pdf/2005.14638.pdf)\n\n[(*) Federated Visual Classification with Real-World Data Distribution. MIT. ECCV 2020. 2020-03](https://arxiv.org/pdf/2003.08082.pdf)\n\n[FedVision: An Online Visual Object Detection Platform Powered by Federated Learning](https://arxiv.org/pdf/2001.06202.pdf)\n\n## Health Care: \n[Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation](https://arxiv.org/pdf/1810.04304.pdf)\n\n[Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data](https://arxiv.org/pdf/1810.08553.pdf)\n\n[Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention](https://arxiv.org/pdf/2006.10517.pdf)\n\n[A Federated Learning Framework for Healthcare IoT devices](https://arxiv.org/pdf/2005.05083.pdf)\nKeywords: Split Learning + Sparsification\n\n[Federated Transfer Learning for EEG Signal Classification](https://arxiv.org/pdf/2004.12321.pdf)\n\n[The Future of Digital Health with Federated Learning](https://arxiv.org/pdf/2003.08119.pdf)\n\n[Anonymizing Data for Privacy-Preserving Federated Learning. ECAI 2020.](https://arxiv.org/pdf/2002.09096.pdf)\n\n[Federated machine learning with Anonymous Random Hybridization (FeARH) on medical records](https://arxiv.org/pdf/2001.09751.pdf)\n\n[Stratified cross-validation for unbiased and privacy-preserving federated learning](https://arxiv.org/pdf/2001.08090.pdf)\n\n[Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results](https://arxiv.org/pdf/2001.05647.pdf)\n\n[Learn Electronic Health Records by Fully Decentralized Federated Learning](https://arxiv.org/pdf/1912.01792.pdf)\n\n[Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortality](https://arxiv.org/pdf/1912.00354.pdf)\n\n[Federated Learning for Healthcare Informatics](https://arxiv.org/pdf/1911.06270.pdf)\n\n[Federated and Differentially Private Learning for Electronic Health Records](https://arxiv.org/pdf/1911.05861.pdf)\n\n[A blockchain-orchestrated Federated Learning architecture for healthcare consortia](https://arxiv.org/pdf/1910.12603.pdf)\n\n[Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data](https://arxiv.org/pdf/1910.12191.pdf)\n\n[Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving](https://arxiv.org/pdf/1910.11160.pdf)\n\n[Differential Privacy-enabled Federated Learning for Sensitive Health Data](https://arxiv.org/pdf/1910.02578.pdf)\n\n[LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0230706)\n\n[Privacy Preserving Stochastic Channel-Based Federated Learning with Neural Network Pruning](https://arxiv.org/pdf/1910.02115.pdf)\n\n[Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence](https://arxiv.org/pdf/1910.02109.pdf)\n\n[Privacy-preserving Federated Brain Tumour Segmentation](https://arxiv.org/pdf/1910.00962.pdf)\n\n[HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography](https://arxiv.org/pdf/1909.05784.pdf)\n\n[FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare](https://arxiv.org/pdf/1907.09173.pdf)\n\n[Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records](https://arxiv.org/pdf/1903.09296.pdf)\n\n[LoAdaBoost:Loss-Based AdaBoost Federated Machine Learning on medical Data](https://arxiv.org/pdf/1811.12629.pdf)\n\n[FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record](https://arxiv.org/pdf/1811.11400.pdf)\n\n\n## Transportation:\n[Federated Learning for Vehicular Networks](https://arxiv.org/pdf/2006.01412.pdf)\n\n[Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach](https://arxiv.org/pdf/2004.03877.pdf)\n\n[Federated Learning Meets Contract Theory: Energy-Efficient Framework for Electric Vehicle Networks](https://arxiv.org/pdf/2004.01828.pdf)\n\n[Beyond privacy regulations: an ethical approach to data usage in transportation. TomTom. 2020-04-01](https://arxiv.org/pdf/2004.00491.pdf)\n\n[Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach](https://arxiv.org/pdf/2003.08725.pdf)\n\n[Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory. 2020-03](https://arxiv.org/pdf/2003.04451.pdf)\n\n[FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing. 2020-03](https://arxiv.org/pdf/2003.03697.pdf)\n\n[Practical Privacy Preserving POI Recommendation](https://arxiv.org/pdf/2003.02834.pdf)\n\n[Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Method](https://arxiv.org/pdf/2001.01911.pdf)\n\n[Federated Transfer Reinforcement Learning for Autonomous Driving](https://arxiv.org/pdf/1910.06001.pdf)\n\n[Energy Demand Prediction with Federated Learning for Electric Vehicle Networks](https://arxiv.org/pdf/1909.00907.pdf)\n\n[Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications](https://arxiv.org/pdf/1807.08127.pdf)\n\n[Federated Learning for Ultra-Reliable Low-Latency V2V Communications](https://arxiv.org/pdf/1805.09253.pdf)\n\n[Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach](https://ieeexplore.ieee.org/abstract/document/8964354/)\n\n\n## Recommendation System\n[(*) Federated Multi-view Matrix Factorization for Personalized Recommendations](https://arxiv.org/pdf/2004.04256.pdf)\n\n\n[Robust Federated Recommendation System](https://arxiv.org/pdf/2006.08259.pdf)\n\n[Federated Recommendation System via Differential Privacy](https://arxiv.org/pdf/2005.06670.pdf)\n\n[FedRec: Privacy-Preserving News Recommendation with Federated Learning. MSRA. 2020-03](https://arxiv.org/pdf/2003.09592.pdf)\n\n[Federating Recommendations Using Differentially Private Prototypes](https://arxiv.org/pdf/2003.00602.pdf)\n\n[Meta Matrix Factorization for Federated Rating Predictions](https://arxiv.org/pdf/1910.10086.pdf)\n\n[Federated Hierarchical Hybrid Networks for Clickbait Detection](https://arxiv.org/pdf/1906.00638.pdf)\n\n[Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System](https://arxiv.org/pdf/1901.09888.pdf)\n\n## Speech Recognition\n[Training Keyword Spotting Models on Non-IID Data with Federated Learning](https://arxiv.org/pdf/2005.10406.pdf)\n\n## Finance\n[FedCoin: A Peer-to-Peer Payment System for Federated Learning](https://arxiv.org/pdf/2002.11711.pdf)\n\n[Towards Federated Graph Learning for Collaborative Financial Crimes Detection](https://arxiv.org/pdf/1909.12946.pdf)\n\n## Smart City\n[Cloud-based Federated Boosting for Mobile Crowdsensing](https://arxiv.org/pdf/2005.05304.pdf)\n\n[Exploiting Unlabeled Data in Smart Cities using Federated Learning](https://arxiv.org/pdf/2001.04030.pdf)\n\n## Robotics\n[Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data](https://arxiv.org/pdf/1909.00895.pdf)\n\n[Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems](https://arxiv.org/pdf/1901.06455.pdf)\n\n## Networking\n[A Federated Learning Approach for Mobile Packet Classification](https://arxiv.org/pdf/1907.13113.pdf)\n\n## Blockchain\n[Blockchained On-Device Federated Learning](https://arxiv.org/pdf/1808.03949.pdf)\n\n[Record and reward federated learning contributions with blockchain](https://mblocklab.com/RecordandReward.pdf)\n\n## Other\n[Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing](https://arxiv.org/pdf/1907.10218.pdf)\n\n[Self-supervised audio representation learning for mobile devices](https://arxiv.org/pdf/1905.11796.pdf)\n\n[Combining Federated and Active Learning for Communication-efficient Distributed Failure Prediction in Aeronautics](https://arxiv.org/pdf/2001.07504.pdf)\n\n[PMF: A Privacy-preserving Human Mobility Prediction Framework via Federated Learning](https://vonfeng.github.io/files/UbiComp2020_PMF_Final.pdf)\n\n[Federated Multi-task Hierarchical Attention Model for Sensor Analytics](https://arxiv.org/pdf/1905.05142.pdf)\n\n[DÏoT: A Federated Self-learning Anomaly Detection System for IoT](https://arxiv.org/pdf/1804.07474.pdf)\n\n# Benchmark, Dataset and Survey \n\n## Benchmark and Dataset\n\n[The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems](https://arxiv.org/pdf/2006.07856.pdf)\n\n[Evaluation Framework For Large-scale Federated Learning](https://arxiv.org/pdf/2003.01575.pdf)\n\n[(*) PrivacyFL: A simulator for privacy-preserving and secure federated learning. MIT CSAIL.](https://arxiv.org/pdf/2002.08423.pdf)\n\n[Revocable Federated Learning: A Benchmark of Federated Forest](https://arxiv.org/pdf/1911.03242.pdf)\n\n[Real-World Image Datasets for Federated Learning](https://arxiv.org/pdf/1910.11089.pdf)\n\n[LEAF: A Benchmark for Federated Settings](https://arxiv.org/pdf/1812.01097.pdf)\n\n[Functional Federated Learning in Erlang (ffl-erl)](https://arxiv.org/pdf/1808.08143.pdf)\n\n## Survey\n\n[A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://arxiv.org/pdf/1907.09693.pdf)\n\nResearcher: Bingsheng He, NUS [Qinbin Li, PhD, NUS, HKUST](https://qinbinli.com/files/CV_QB.pdf)\n\n[SECure: A Social and Environmental Certificate for AI Systems](https://arxiv.org/pdf/2006.06217.pdf)\n\n[From Federated Learning to Fog Learning: Towards Large-Scale Distributed Machine Learning in Heterogeneous Wireless Networks](https://arxiv.org/pdf/2006.03594.pdf)\n\n[Federated Learning for 6G Communications: Challenges, Methods, and Future Directions](https://arxiv.org/pdf/2006.02931.pdf)\n\n[A Review of Privacy Preserving Federated Learning for Private IoT Analytics](https://arxiv.org/pdf/2004.11794.pdf)\n\n[Survey of Personalization Techniques for Federated Learning. 2020-03-19](https://arxiv.org/pdf/2003.08673.pdf)\n\n[Threats to Federated Learning: A Survey](https://arxiv.org/pdf/2003.02133.pdf)\n\n[Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective](https://arxiv.org/pdf/2002.11545.pdf)\n\n[Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art](https://arxiv.org/pdf/2002.10610.pdf)\n\n[Advances and Open Problems in Federated Learning](https://arxiv.org/pdf/1912.04977.pdf)\n\n[Privacy-Preserving Blockchain Based Federated Learning with Differential Data Sharing](https://arxiv.org/pdf/1912.04859.pdf)\n\n[An Introduction to Communication Efficient Edge Machine Learning](https://arxiv.org/pdf/1912.01554.pdf)\n\n[Federated Learning for Healthcare Informatics](https://arxiv.org/pdf/1911.06270.pdf)\n\n[Federated Learning for Coalition Operations](https://arxiv.org/pdf/1910.06799.pdf)\n\n[Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://arxiv.org/pdf/1909.11875.pdf)\n\n[Federated Learning: Challenges, Methods, and Future Directions](https://arxiv.org/pdf/1908.07873.pdf)\n\n[A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://arxiv.org/pdf/1907.09693.pdf)\n\n[Federated Machine Learning: Concept and Applications](https://arxiv.org/pdf/1902.04885.pdf)\n\n[No Peek: A Survey of private distributed deep learning](https://arxiv.org/pdf/1812.03288.pdf)\n\n[Communication-Efficient Edge AI: Algorithms and Systems](http://arxiv.org/pdf/2002.09668.pdf)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchaoyanghe%2FAwesome-Federated-Learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchaoyanghe%2FAwesome-Federated-Learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchaoyanghe%2FAwesome-Federated-Learning/lists"}