{"id":13444453,"url":"https://github.com/poga/awesome-federated-learning","last_synced_at":"2026-01-26T18:54:10.666Z","repository":{"id":39608677,"uuid":"220137189","full_name":"poga/awesome-federated-learning","owner":"poga","description":"resources about federated learning and privacy in machine 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Learning","Table of Contents","Privacy \u0026 Safety","Others"],"sub_categories":["Uncategorized"],"readme":"# Awesome Federated Learning [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)\n\nA list of resources releated to federated learning and privacy in machine learning.\n\n## Related Awesome Lists\n\n* [tushar-semwal/awesome-federated-computing](https://github.com/tushar-semwal/awesome-federated-computing)\n\n## Papers\n\n### Introduction \u0026 Survey\n\n* Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies https://ieeexplore.ieee.org/document/9780218\n\n* The Internet of Federated Things (IoFT) https://ieeexplore.ieee.org/document/9611259\n\n* Advances and Open Problems in Federated Learning https://arxiv.org/pdf/1912.04977.pdf\n\n* Federated Machine Learning: Concept and Applications https://arxiv.org/pdf/1902.04885\n\n* Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection https://arxiv.org/abs/1907.09693\n\n* Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis https://arxiv.org/abs/1802.09941\n\n* EdgeAI: A Visionfor Deep Learning in IoT Era https://arxiv.org/abs/1910.10356\n\n* Machine Learning Systems for Highly-Distributed and Rapidly-Growing Data https://arxiv.org/abs/1910.08663\n\n* No Peek: A Survey of private distributed deep learning https://arxiv.org/pdf/1812.03288\n\n* Federated Learning in Mobile Edge Networks: A Comprehensive Survey https://arxiv.org/abs/1909.11875\n\n### Privacy and Security\n\n* Federated Learning with Formal Differential Privacy Guarantees https://ai.googleblog.com/2022/02/federated-learning-with-formal.html\n\n* Applying Differential Privacy to Large Scale Image Classification https://ai.googleblog.com/2022/02/applying-differential-privacy-to-large.html\n\n* Towards Causal Federated Learning For Enhanced Robustness And Privacy https://arxiv.org/pdf/2104.06557.pdf ICLR DPML 2021\n\n* FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning https://arxiv.org/abs/2102.02514\n\n* OpenFL: An open-source framework for Federated Learning https://arxiv.org/abs/2105.06413\n\n* A Bayesian Federated Learning Framework with Multivariate Gaussian Product https://arxiv.org/abs/2102.01936\n\n* Communication-Efficient Learning of Deep Networks from Decentralized Data https://arxiv.org/pdf/1602.05629.pdf\n\n* Practical Secure Aggregation for Federated Learning on User-Held Data https://arxiv.org/abs/1611.04482\n\n* Practical Secure Aggregation for Privacy-Preserving Machine Learning https://storage.googleapis.com/pub-tools-public-publication-data/pdf/ae87385258d90b9e48377ed49d83d467b45d5776.pdf\n\n* A Hybrid Approach to Privacy-Preserving Federated Learning https://arxiv.org/abs/1812.03224\n\n* Analyzing Federated Learning through an Adversarial Lens https://arxiv.org/pdf/1811.12470\n\n* How To Backdoor Federated Learning https://arxiv.org/abs/1807.00459\n\n* Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attack https://arxiv.org/abs/1812.00910\n\n* Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning https://arxiv.org/pdf/1812.00535\n\n* Exploiting Unintended Feature Leakage in Collaborative Learning https://arxiv.org/abs/1805.04049\n\n* Analyzing Federated Learning through an Adversarial Lens https://arxiv.org/abs/1811.12470\n\n* Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning https://arxiv.org/abs/1702.07464\n\n* Protection Against Reconstruction and Its Applications in Private Federated Learning https://arxiv.org/pdf/1812.00984\n\n* Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing https://arxiv.org/abs/1907.10218\n\n* Differentially Private Data Generative Models  https://arxiv.org/pdf/1812.02274\n\n* Differentially Private Federated Learning: A Client Level Perspective https://arxiv.org/abs/1712.07557\n\n* Privacy-Preserving Collaborative Deep Learning with Unreliable Participants https://arxiv.org/abs/1812.10113\n\n* Scalable Private Learning with PATE https://arxiv.org/abs/1802.08908\n\n* Reducing leakage in distributed deep learning for sensitive health data https://www.media.mit.edu/publications/reducing-leakage-in-distributed-deep-learning-for-sensitive-health-data-accepted-to-iclr-2019-workshop-on-ai-for-social-good-2019/\n\n* Deep Leakage from Gradients http://papers.nips.cc/paper/9617-deep-leakage-from-gradients.pdf\n\n* Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning https://arxiv.org/abs/1805.05838\n\n\n### System and Application\n\n* Pisces: Efficient Federated Learning via Guided Asynchronous Training https://dl.acm.org/doi/abs/10.1145/3542929.3563463\n\n* Record and Reward Federated Learning Contributions with Blockchain https://mblocklab.com/RecordandReward.pdf\n\n* Flower: A Friendly Federated Learning Framework https://arxiv.org/pdf/2007.14390.pdf\n\n* Learning Private Neural Language Modeling with Attentive Aggregation https://arxiv.org/pdf/1812.07108\n\n* Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning https://arxiv.org/abs/2003.09603\n\n* Decentralized Knowledge Acquisition for Mobile Internet Applications https://link.springer.com/article/10.1007/s11280-019-00775-w\n\n* A generic framework for privacy preserving deep learning https://arxiv.org/pdf/1811.04017.pdf\n\n* Federated Learning of N-gram Language Models https://arxiv.org/pdf/1910.03432.pdf\n\n* Towards Federated Learning at Scale: System Design https://arxiv.org/pdf/1902.01046.pdf\n\n* Federated Learning for Keyword Spotting https://arxiv.org/abs/1810.05512\n\n* Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data https://arxiv.org/abs/1810.08553\n\n* Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System https://arxiv.org/pdf/1901.09888\n\n* Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence https://arxiv.org/abs/1910.02109\n\n* Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platform http://www.cs.ucf.edu/~mohaisen/doc/dsn19b.pdf\n\n* Institutionally Distributed Deep Learning Networks https://arxiv.org/abs/1709.05929\n\n* Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation https://arxiv.org/abs/1810.04304\n\n* Split learning for health: Distributed deep learning without sharing raw patient data https://www.media.mit.edu/publications/split-learning-for-health-distributed-deep-learning-without-sharing-raw-patient-data/\n\n* Continuous Delivery for Machine Learning https://martinfowler.com/articles/cd4ml.html#EvolvingIntelligentSystemsWithoutBias\n\n* Ease.ml/ci \u0026 Ease.ml/meter Towards Data Management for Statistical Generialization http://ease.ml/\n\n* VisionAir: Using Federated Learning to estimate Air Quality using the Tensorflow API for Java https://blog.tensorflow.org/2020/02/visionair-using-federated-learning-to-estimate-airquality-tensorflow-api-java.html\n\n* Federated Optimization in Heterogeneous Networks https://arxiv.org/abs/1812.06127\n\n\n\n### Un-org\n\n* FedProf: Optimizing Federated Learning with Dynamic Data Profiling https://arxiv.org/abs/2102.01733\n\n* FedBN: Federated Learning on Non-IID Features via Local Batch Normalization https://arxiv.org/abs/2102.07623\n\n* A Scalable Approach for Partially Local Federated Learning https://ai.googleblog.com/2021/12/a-scalable-approach-for-partially-local.html?m=1\n\n* Federated Visual Classification with Real-World Data Distribution https://arxiv.org/abs/2003.08082\n\n* Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification https://arxiv.org/abs/1909.06335\n\n* LEAF: A Benchmark for Federated Settings https://arxiv.org/abs/1812.01097\n\n* On the Convergence of FedAvg on Non-IID Data https://arxiv.org/abs/1907.02189\n\n* Privacy-preserving Federated Brain Tumour Segmentation. https://arxiv.org/pdf/1910.00962.pdf\n\n* ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries https://www.media.mit.edu/publications/ExpertMatcher/\n\n* Detailed comparison of communication efficiency of split learning and federated learning https://www.media.mit.edu/publications/detailed-comparison-of-communication-efficiency-of-split-learning-and-federated-learning-1/\n\n* Split Learning: Distributed and collaborative learning https://aiforsocialgood.github.io/iclr2019/accepted/track1/pdfs/31_aisg_iclr2019.pdf\n\n* Asynchronous Federated Optimization https://arxiv.org/pdf/1903.03934\n\n* Robust and Communication-Efficient Federated Learning from Non-IID Data https://arxiv.org/pdf/1903.02891\n\n* One-Shot Federated Learning https://arxiv.org/pdf/1902.11175\n\n* High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions https://arxiv.org/pdf/1902.08999\n\n* Agnostic Federated Learning https://arxiv.org/pdf/1902.00146%C2%A0\n\n* Peer-to-peer Federated Learning on Graphs https://arxiv.org/pdf/1901.11173\n\n* SecureBoost: A Lossless Federated Learning Framework https://arxiv.org/pdf/1901.08755\n\n* Federated Reinforcement Learning https://arxiv.org/pdf/1901.08277\n\n* Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems https://arxiv.org/pdf/1901.06455\n\n* Federated Learning via Over-the-Air Computation https://arxiv.org/pdf/1812.11750\n\n* Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version) https://arxiv.org/pdf/1812.11494\n\n* Multi-objective Evolutionary Federated Learning https://arxiv.org/pdf/1812.07478\n\n* Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach https://arxiv.org/pdf/1812.03633\n\n* A Hybrid Approach to Privacy-Preserving Federated Learning https://arxiv.org/pdf/1812.03224\n\n* Applied Federated Learning: Improving Google Keyboard Query Suggestions https://arxiv.org/pdf/1812.02903\n\n* Differentially Private Data Generative Models https://arxiv.org/pdf/1812.02274\n\n* Protection Against Reconstruction and Its Applications in Private Federated Learning https://arxiv.org/pdf/1812.00984\n\n* Split learning for health: Distributed deep learning without sharing raw patient data https://arxiv.org/pdf/1812.00564\n\n* Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning https://arxiv.org/pdf/1812.00535\n\n* LoAdaBoost:Loss-Based AdaBoost Federated Machine Learning on medical Data https://arxiv.org/pdf/1811.12629\n\n* Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data https://arxiv.org/pdf/1811.11479\n\n* Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning https://arxiv.org/pdf/1811.09904\n\n* Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting https://arxiv.org/pdf/1811.09712\n\n* Federated Learning Approach for Mobile Packet Classification https://arxiv.org/abs/1907.13113\n\n* Collaborative Learning on the Edges: A Case Study on Connected Vehicles https://www.usenix.org/conference/hotedge19/presentation/lu\n\n* Federated Learning for Time Series Forecasting Using Hybrid Model http://www.diva-portal.se/smash/get/diva2:1334629/FULLTEXT01.pdf\n\n* Federated Learning: Challenges, Methods, and Future Directions https://arxiv.org/pdf/1908.07873.pdf\n\n* Federated Learning with Matched Averaging https://openreview.net/forum?id=BkluqlSFDS\n\n\n\n## Code\n\n* OpenFL: An open-source framework for Federated Learning - https://github.com/intel/openfl\n\n* Flower https://flower.ai/\n\n* PySyft https://github.com/OpenMined/PySyft\n\n* Tensorflow Federated  https://www.tensorflow.org/federated\n\n* CrypTen https://github.com/facebookresearch/CrypTen\n\n* FATE https://fate.fedai.org/\n\n* DVC https://dvc.org/\n\n* LEAF https://leaf.cmu.edu/\n\n* Federated iNaturalist/Landmarkds https://github.com/google-research/google-research/tree/master/federated_vision_datasets\n\n* FedML: A Research Library and Benchmark for Federated Machine Learning https://github.com/FedML-AI/FedML\n\n* XayNet: Open source framework for federated learning in Rust https://xaynet.webflow.io/\n\n* EnvisEdge: https://github.com/NimbleEdge/EnvisEdge\n\n\n## Use-cases\n\nMIT CSAIL/Harvard Medical/Tsinghua University’s Academy of Arts and Design\n\n* https://arxiv.org/ftp/arxiv/papers/1903/1903.09296.pdf\n* https://venturebeat.com/2019/03/25/federated-learning-technique-predicts-hospital-stay-and-patient-mortality/\n\nMicrosoft research/University of Chinese Academy of Sciences, Beijing, China\n\n* https://arxiv.org/pdf/1907.09173.pdf\n\nBoston University/Massachusetts General Hospital\n\n* https://www.ncbi.nlm.nih.gov/pubmed/29500022\n\nGoogle\n\n* https://ai.googleblog.com/2017/04/federated-learning-collaborative.html\n* https://www.statnews.com/2019/09/10/google-mayo-clinic-partnership-patient-data/\n\nTencent WeBank\n\n* https://www.digfingroup.com/webank-clustar/\n\nNvidia/King’s College London, American College of Radiology, MGH and BWH Center for Clinical Data Science, and UCLA Health... etc\n\n* https://venturebeat.com/2019/10/13/nvidia-uses-federated-learning-to-create-medical-imaging-ai/\n* https://blogs.nvidia.com/blog/2019/12/01/clara-federated-learning/\n\n\n\n\n\n## Company\n\n\n* integrate.ai https://integrate.ai\n    * IntegrateFL: A SaaS platform for Federated Learning https://integrate.ai/integratefl/\n\n* Adap https://adap.com/en\n\n* Snips\n    * https://snips.ai/\n    * https://www.theverge.com/2019/11/21/20975607/sonos-buys-snips-ai-voice-assistant-privacy\n\n* Privacy.ai https://privacy.ai/\n\n* OpenMined https://www.openmined.org/\n\n* Arkhn https://arkhn.org/en/\n\n* Scaleout https://scaleoutsystems.com/\n\n* MELLODDY https://www.melloddy.eu/\n\n* DataFleets https://www.datafleets.com/\n\n* Xayn AG https://www.xayn.com/\n\n* NimbleEdge https://www.nimbleedge.ai/\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpoga%2Fawesome-federated-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpoga%2Fawesome-federated-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpoga%2Fawesome-federated-learning/lists"}