{"id":13444455,"url":"https://github.com/tushar-semwal/awesome-federated-computing","last_synced_at":"2025-03-20T18:32:43.205Z","repository":{"id":37734952,"uuid":"173286323","full_name":"tushar-semwal/awesome-federated-computing","owner":"tushar-semwal","description":":books: :eyeglasses: A collection of research papers, codes, tutorials and blogs on Federated Computing/Learning.","archived":false,"fork":false,"pushed_at":"2023-08-01T04:26:34.000Z","size":141,"stargazers_count":460,"open_issues_count":0,"forks_count":84,"subscribers_count":35,"default_branch":"master","last_synced_at":"2024-05-19T18:05:53.817Z","etag":null,"topics":["awesome-list","decentralized","deep-learning","distributed-computing","federated-learning","machine-learning"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc0-1.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/tushar-semwal.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2019-03-01T10:53:13.000Z","updated_at":"2024-05-10T05:53:07.000Z","dependencies_parsed_at":"2024-01-11T23:22:51.464Z","dependency_job_id":"3b671d76-421b-48ac-8229-2cbb0b9a6e8c","html_url":"https://github.com/tushar-semwal/awesome-federated-computing","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/tushar-semwal%2Fawesome-federated-computing","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tushar-semwal%2Fawesome-federated-computing/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tushar-semwal%2Fawesome-federated-computing/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tushar-semwal%2Fawesome-federated-computing/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tushar-semwal","download_url":"https://codeload.github.com/tushar-semwal/awesome-federated-computing/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":221792892,"owners_count":16881289,"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":["awesome-list","decentralized","deep-learning","distributed-computing","federated-learning","machine-learning"],"created_at":"2024-07-31T04:00:23.583Z","updated_at":"2024-10-28T06:30:58.091Z","avatar_url":"https://github.com/tushar-semwal.png","language":null,"funding_links":[],"categories":["Uncategorized","Others","Table of Contents","Related Awesome Lists","Other Lists","Awesome Privacy Engineering [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)"],"sub_categories":["Uncategorized","TeX Lists","Other Awesome Privacy Curations"],"readme":"# Awesome Federated Computing [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)\nA collection of research papers, codes, tutorials and blogs on ML carried out in a federated manner (distributed;decentralized).\n\n**Maintainer** - [Tushar Semwal](https://github.com/tushar-semwal)\n\n*Please feel free to shoot me a PR.😊\n\n## Contents\n  - [Blogs](#blogs)\n  - [Survey Papers](#survey-papers)\n  - [Research Papers](#research-papers)\n    - [2022](#2022)\n    - [2021](#2021)\n    - [2020](#2020)\n    - [2019](#2019)\n    - [2018](#2018)\n    - [2017](#2017)\n    - [2016](#2016)\n    - [2015](#2015)\n  - [Libraries/Frameworks](#librariesframeworks)\n  - [Tutorials](#tutorials)\n  - [Datasets](#datasets)\n  - [Projects](#projects)\n\n## Blogs\n* [Learn to adapt Flower for your use-case](https://flower.dev/blog)\n* [Online Comic from Google AI on Federated Learning](https://federated.withgoogle.com/)\n* [Federated Learning: Collaborative Machine Learning without Centralized Training Data](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html)\n* [An Introduction to Federated Learning](http://vision.cloudera.com/an-introduction-to-federated-learning/)\n* [Federated learning: Distributed machine learning with data locality and privacy](https://blog.fastforwardlabs.com/2018/11/14/federated-learning.html)\n* [Federated Learning: The Future of Distributed Machine Learning](https://medium.com/syncedreview/federated-learning-the-future-of-distributed-machine-learning-eec95242d897)\n* [Federated Learning for Wake Word Detection](https://medium.com/snips-ai/federated-learning-for-wake-word-detection-c8b8c5cdd2c5)\n\n## Survey Papers\n* [Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies](https://ieeexplore.ieee.org/document/9780218), IEEE TBD 2022\n* [Survey of Personalization Techniques for Federated Learning](https://arxiv.org/pdf/2003.08673.pdf), preprint 2020\n* [Advances and Open Problems in Federated Learning](https://hal.inria.fr/hal-02406503/document), HAL-Inria 2019\n* [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://arxiv.org/abs/1909.11875), preprint 2019\n* [Federated Machine Learning: Concept and Applications](https://dl.acm.org/citation.cfm?id=3298981), ACM TIST 2019\n* [Federated Learning: Challenges, Methods, and Future Directions](https://arxiv.org/pdf/1908.07873.pdf), 2019\n* [Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://arxiv.org/pdf/1907.09693.pdf), 2019\n* [Convergence of Edge Computing and Deep Learning: A Comprehensive Survey](https://arxiv.org/pdf/1907.08349.pdf), preprint, 2019\n  \n## Research Papers\n\n### 2022\n* [Pisces: Efficient Federated Learning via Guided Asynchronous Training](https://dl.acm.org/doi/abs/10.1145/3542929.3563463), ACM SoCC 2022\n\n### 2021\n* [Towards Causal Federated Learning For Enhanced Robustness And Privacy](https://arxiv.org/pdf/2104.06557.pdf), ICLR DPML 2021\n* [FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning](https://arxiv.org/abs/2102.02514), preprint\n* [OpenFL: An open-source framework for Federated Learning](https://arxiv.org/abs/2105.06413), preprint. [[code](https://github.com/intel/openfl)] ![alt text](https://github.com/tushar-semwal/awesome-federated-computing/blob/master/imgs/github-logo-16px.png)\n* [A Bayesian Federated Learning Framework with Multivariate Gaussian Product](https://arxiv.org/abs/2102.01936), preprint\n* [Client Selection for Federated Learning With Non-IID Data in Mobile Edge Computing](https://ieeexplore.ieee.org/abstract/document/9345723), IEEE Access\n* [FedProf: Optimizing Federated Learning with Dynamic Data Profiling](https://arxiv.org/abs/2102.01733), ICML2020\n* [Toward Resource-Efficient Federated Learning in Mobile Edge Computing](https://ieeexplore.ieee.org/abstract/document/9354925?casa_token=UUM11Ln9a_MAAAAA:I_t1tncSN1LMKrfFZ1dH-hLma2eTvy68Xw1zYZH-29e9jTTLwK3Eu53q-74-IoeZykALKWXvSg), IEEE Network\n* [Scaling Neuroscience Research using Federated Learning](https://arxiv.org/abs/2102.08440), preprint\n* [Exploiting Shared Representations for Personalized Federated Learning](https://arxiv.org/abs/2102.07078), preprint\n* [FedSA: A staleness-aware asynchronous Federated Learning algorithm with non-IID data](https://www.sciencedirect.com/science/article/abs/pii/S0167739X21000649), FGCS Elsevier\n* [Blockchained Federated Learning for Threat Defense](https://arxiv.org/abs/2102.12746)\n* [Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning](https://arxiv.org/abs/2102.12920), preprint\n* [FedBN: Federated Learning on Non-IID Features via Local Batch Normalization](https://arxiv.org/abs/2102.07623), ICLR 2021 [[code](https://github.com/med-air/FedBN)] ![alt text](https://github.com/tushar-semwal/awesome-federated-computing/blob/master/imgs/github-logo-16px.png)\n* [Federated Learning over Noisy Channels: Convergence Analysis and Design Examples](https://arxiv.org/abs/2101.02198), preprint\n* [FedSim: Similarity guided model aggregation for Federated Learning](https://www.sciencedirect.com/science/article/abs/pii/S0925231221016039), Neurocomputing Journal, [[code](https://github.com/chamathpali/FedSim)] ![alt text](https://github.com/tushar-semwal/awesome-federated-computing/blob/master/imgs/github-logo-16px.png)\n### 2020\n* [Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data](https://www.nature.com/articles/s41598-020-69250-1), Nature Scientific Reports.\n* [Multi-Center Federated Learning](https://arxiv.org/abs/2005.01026), preprint [[code](https://github.com/anonymousgit2020/fedsem)] ![alt text](https://github.com/tushar-semwal/awesome-federated-computing/blob/master/imgs/github-logo-16px.png)\n* [Turn Signal Prediction: A Federated Learning Case Study](https://arxiv.org/abs/2012.12401), preprint\n* [FedPerf: A Practitioners’ Guide to Performance of Federated Learning Algorithms](https://osf.io/q3vkt), preprint\n* [WAFFLe: Weight Anonymized Factorization for Federated Learning](https://arxiv.org/abs/2008.05687), preprint\n* [Fed+: A Family of Fusion Algorithms for Federated Learning](https://arxiv.org/abs/2009.06303), preprint\n* [Fast-Convergent Federated Learning](https://arxiv.org/pdf/2007.13137.pdf), preprint\n* [FedML: A Research Library and Benchmark for Federated Machine Learning](https://arxiv.org/abs/2007.13518), preprint [[code](https://github.com/FedML-AI/FedML)] ![alt text](https://github.com/tushar-semwal/awesome-federated-computing/blob/master/imgs/github-logo-16px.png)\n* [Federated Visual Classification with Real-World Data Distribution](https://arxiv.org/abs/2003.08082), ECCV 2020 [[code](https://github.com/google-research/google-research/tree/master/federated_vision_datasets)] ![alt text](https://github.com/tushar-semwal/awesome-federated-computing/blob/master/imgs/github-logo-16px.png)\n* [Flower: A Friendly Federated Learning Research Framework](https://arxiv.org/abs/2007.14390), preprint [[code](https://flower.dev/)] ![alt text](https://github.com/tushar-semwal/awesome-federated-computing/blob/master/imgs/github-logo-16px.png)\n* [Federated Deep Reinforcement Learning for Internet of Things with Decentralized Cooperative Edge Caching](https://doi.org/10.1109/JIOT.2020.2986803), IEEE IoT journal\n* [Three Approaches for Personalization with Applications to Federated Learning](https://arxiv.org/abs/2002.10619), preprint\n* [Salvaging Federated Learning by Local Adaptation](https://arxiv.org/abs/2002.04758), preprint\n* [Federated Learning of a Mixture of Global and Local Models](https://arxiv.org/abs/2002.05516), preprint\n* [Federated Learning with Matched Averaging](https://arxiv.org/pdf/2002.06440.pdf), ICLR 2020\n* [On the Convergence of FedAvg on Non-IID Data](https://arxiv.org/abs/1907.02189), ICLR 2020. [[code](https://github.com/lx10077/fedavgpy)] ![alt text](https://github.com/tushar-semwal/awesome-federated-computing/blob/master/imgs/github-logo-16px.png)\n* [Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning](https://arxiv.org/abs/2003.09603), preprint\n* [Knowledge Federation: Hierarchy and Unification](https://arxiv.org/pdf/2002.01647.pdf), preprint\n* [Decentralized Knowledge Acquisition for Mobile Internet Applications](https://link.springer.com/article/10.1007/s11280-019-00775-w), World Wide Web, Springer journal\n* [Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach](https://ieeexplore.ieee.org/abstract/document/8964354), IEEE Access 2020  \n* [Multi-Participant Multi-Class Vertical Federated Learning](https://arxiv.org/pdf/2001.11154.pdf), preprint\n* [Accelerating DNN Training in Wireless Federated Edge Learning System](https://arxiv.org/pdf/1905.09712.pdf), preprint \n* [Federated Optimization in Heterogeneous Networks](https://arxiv.org/abs/1812.06127), MLSYS 2020 [[code](https://github.com/litian96/FedProx)] ![alt text](https://github.com/tushar-semwal/awesome-federated-computing/blob/master/imgs/github-logo-16px.png)\n* [A Secure Federated Transfer Learning Framework](https://ieeexplore.ieee.org/document/9076003) IEEE Intelligent Systems 2020\n* [Federated Learning for Healthcare Informatics](https://arxiv.org/abs/1911.06270), preprint\n\n### 2019\n* [Record and Reward Federated Learning Contributions with Blockchain](https://mblocklab.com/RecordandReward.pdf), IEEE CyberC 2019, [[Code]](https://github.com/sreyafrancis/BlockchainForFederatedLearning) ] ![alt text](https://github.com/tushar-semwal/awesome-federated-computing/blob/master/imgs/github-logo-16px.png)\n* [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://arxiv.org/abs/1910.01991), IEEE Transaction on Neural Nets, [[Code](https://github.com/felisat/clustered-federated-learning)] ![alt text](https://github.com/tushar-semwal/awesome-federated-computing/blob/master/imgs/github-logo-16px.png)\n* [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://arxiv.org/abs/1903.02891), preprint. [[Code](https://github.com/felisat/federated-learning)] ![alt text](https://github.com/tushar-semwal/awesome-federated-computing/blob/master/imgs/github-logo-16px.png)\n* [Federated Learning for Ranking Browser History Suggestions](https://arxiv.org/abs/1911.11807), preprint. [[Code](https://github.com/florian/federated-learning)] ![alt text](https://github.com/tushar-semwal/awesome-federated-computing/blob/master/imgs/github-logo-16px.png)\n* [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://arxiv.org/abs/1909.06335), preprint\n* [The Non-IID Data Quagmire of Decentralized Machine Learning](https://arxiv.org/abs/1910.00189), preprint\n* [Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning](https://arxiv.org/abs/1805.05838), preprint\n* [Accelerating DNN Training in Wireless Federated Edge Learning System](https://arxiv.org/abs/1905.09712), preprint\n* [FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record](https://arxiv.org/pdf/1811.11400.pdf), NIPS 2018 Workshop \n* [Federated Learning for Emoji Prediction in a Mobile Keyboard](https://arxiv.org/pdf/1906.04329.pdf), preprint\n* [Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems](https://arxiv.org/abs/1901.06455), IEEE RAL 2019\n* [Decentralized Federated Learning: A Segmented Gossip Approach](https://arxiv.org/abs/1908.07782), FML 2019\n* [Federated Learning-Based Computation Offloading Optimization in Edge Computing-Supported Internet of Things](https://ieeexplore.ieee.org/document/8728285), IEEE Access\n* [Towards Faster and Better Federated Learning: A Feature Fusion Approach](https://ieeexplore.ieee.org/abstract/document/8803001/), ICIP 2019\n* [Decentralized Bayesian Learning over Graphs](https://arxiv.org/pdf/1905.10466.pdf), preprint\n* [Federated Multi-task Hierarchical Attention Model for Sensor Analytics](https://arxiv.org/pdf/1905.05142.pdf), preprint\n* [FFD: A Federated Learning Based Method for Credit Card Fraud Detection](https://link.springer.com/chapter/10.1007/978-3-030-23551-2_2), International Conference on Big Data 2019.\n* [Federated Learning for Time Series Forecasting Using LSTM Networks: Exploiting Similarities Through Clustering](http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1334598\u0026dswid=-6117), Student thesis, KTH\n* [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://arxiv.org/abs/1804.05271), IEEE JSAC.\n* [Privacy-Preserving Deep Learning via Weight Transmission](https://arxiv.org/abs/1809.03272)\n* [Learning Private Neural Language Modeling with Attentive Aggregation](https://arxiv.org/pdf/1812.07108), IJCNN 2019. [[Code](https://github.com/shaoxiongji/fed-att)] ![alt text](https://github.com/tushar-semwal/awesome-federated-computing/blob/master/imgs/github-logo-16px.png)\n* [On the Convergence of FedAvg on Non-IID Data](https://arxiv.org/abs/1907.02189), preprint. [[code](https://github.com/lx10077/fedavgpy)] ![alt text](https://github.com/tushar-semwal/awesome-federated-computing/blob/master/imgs/github-logo-16px.png)\n* [Federated Learning of Out-of-Vocabulary Words](https://arxiv.org/pdf/1903.10635.pdf)\n* [Towards Federated Learning at Scale: System Design](https://arxiv.org/abs/1902.01046)\n* [Agnostic Federated Learning](https://arxiv.org/abs/1902.00146) preprint 2019\n* [Peer-to-peer Federated Learning on Graphs](https://arxiv.org/abs/1901.11173) preprint \n### 2018\n* [A Performance Evaluation of Federated Learning Algorithms](https://dl.acm.org/doi/10.1145/3286490.3286559), DIDL 2018\n* [How to backdoor federated learning](https://arxiv.org/pdf/1807.00459), preprint\n* [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://arxiv.org/abs/1804.08333)\n* [Federated Kernelized Multi-Task Learning](http://www.sysml.cc/doc/30.pdf)\n* [Federated Learning with Non-IID Data](https://arxiv.org/abs/1806.00582), preprint.\n* [Distributed Fine-tuning of Language Models on Private Data](https://openreview.net/pdf?id=HkgNdt26Z), ICLR 2018\n* [Federated Learning Based Proactive Content Caching in Edge Computing](https://ieeexplore.ieee.org/abstract/document/8647616/), IEEE GLOBECOM 2018\n* [When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning](http://www.commsp.ee.ic.ac.uk/~wiser/dais-ita/tiffany_papers/infocom_2018.pdf), IEEE Infocom 2018\n* [How To Backdoor Federated Learning](https://arxiv.org/abs/1807.00459)\n* [LEAF: A Benchmark for Federated Settings](https://arxiv.org/abs/1812.01097), preprint. [[code](https://github.com/TalwalkarLab/leaf)] ![alt text](https://github.com/tushar-semwal/awesome-federated-computing/blob/master/imgs/github-logo-16px.png) \n* [Federated Learning for Mobile Keyboard Prediction - Gboard](https://arxiv.org/abs/1811.03604)\n* [Federated learning of predictive models from federated Electronic Health Records](https://pubmed.ncbi.nlm.nih.gov/29500022/) PMID 2018\n\n### 2017\n* [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://arxiv.org/abs/1602.05629), AISTATS 2017\n* [Differentially Private Federated Learning: A Client Level Perspective](https://arxiv.org/abs/1712.07557), NIPS 2017 Workshop. [[code](https://github.com/SAP/machine-learning-diff-private-federated-learning)] ![alt text](https://github.com/tushar-semwal/awesome-federated-computing/blob/master/imgs/github-logo-16px.png)\n* [Federated Tensor Factorization for Computational Phenotyping](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5652331/), KDD 2017 \n* [Federated Multi-Task Learning](http://papers.nips.cc/paper/7029-federated-multi-task-learning.pdf), NIPS 2017\n### 2016\n* [Practical Secure Aggregation for Federated Learning on User-Held Data](https://arxiv.org/abs/1611.04482), preprint\n* [Federated Learning: Strategies for Improving Communication Efficiency](https://arxiv.org/abs/1610.05492), preprint\n* [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://arxiv.org/abs/1610.02527), preprint\n* [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://arxiv.org/abs/1602.05629), preprint (first FL paper by Google)\n### 2015\n* [Privacy-Preserving Deep Learning](https://www.comp.nus.edu.sg/~reza/files/Shokri-CCS2015.pdf), ACM SIGSAC 2015\n\n## Libraries/Frameworks\n* [PySyft - Github](https://github.com/OpenMined/PySyft) - The PyTorch based library.\n* [Tensorflow Federated - TFF](https://www.tensorflow.org/federated) - A library on top of Tensorflow.\n* [Industrial Federated Learning Framework](https://github.com/WeBankFinTech/FATE), Federated AI Technology Enabler, WeBank AI\n* [PyTorch Federated Learning - Github](https://github.com/shaoxiongji/federated-learning)\n* [Paddle Federated Learning](https://github.com/PaddlePaddle/PaddleFL) - Federated Deep Learning in PaddlePaddle.\n* [Flower](https://flower.dev/) - A friendly federated learning research framework.\n* [OpenFL](https://github.com/intel/openfl) - An open-source framework for Federated Learning on top of TF/PyTorch/etc. \n\n## Tutorials\n* [Flower](https://flower.dev/docs/example_walkthrough_pytorch_mnist.html)\n* [PySyft](https://github.com/OpenMined/PySyft/tree/dev/examples/tutorials)\n* [TFF](https://www.tensorflow.org/federated/tutorials/federated_learning_for_image_classification) \n* [OpenFL](https://github.com/intel/openfl/tree/develop/openfl-tutorials/)\n\n## Datasets\n* [LEAF](https://github.com/TalwalkarLab/leaf)\n* [Federated iNaturalist/Landmarks](https://github.com/google-research/google-research/tree/master/federated_vision_datasets)\n\n## Projects\n* PhotoLabeller by [Jose A. Corbacho](https://github.com/mccorby)\n  - [Client](https://github.com/mccorby/PhotoLabeller)\n  - [Server](https://github.com/mccorby/PhotoLabellerServer)\n* Ownership Protocol by [Qibing Lee](https://github.com/ownership-labs)\n  - [DataToken](https://github.com/ownership-labs/DataToken)\n  - [Compute-to-Data](https://github.com/ownership-labs/Compute-to-Data)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftushar-semwal%2Fawesome-federated-computing","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftushar-semwal%2Fawesome-federated-computing","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftushar-semwal%2Fawesome-federated-computing/lists"}