Awesome Federated Learning

A list of resources releated to federated learning and privacy in machine learning.

Related Awesome Lists

• tushar-semwal/awesome-federated-computing

Papers

Introduction & Survey

• Advances and Open Problems in Federated Learning https://arxiv.org/pdf/1912.04977.pdf

• Federated Machine Learning: Concept and Applications https://arxiv.org/pdf/1902.04885

• Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection https://arxiv.org/abs/1907.09693

• Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis https://arxiv.org/abs/1802.09941

• EdgeAI: A Visionfor Deep Learning in IoT Era https://arxiv.org/abs/1910.10356

• Machine Learning Systems for Highly-Distributed and Rapidly-Growing Data https://arxiv.org/abs/1910.08663

• No Peek: A Survey of private distributed deep learning https://arxiv.org/pdf/1812.03288

• Federated Learning in Mobile Edge Networks: A Comprehensive Survey https://arxiv.org/abs/1909.11875

Privacy and Security

• Communication-Efficient Learning of Deep Networks from Decentralized Data https://arxiv.org/pdf/1602.05629.pdf

• Practical Secure Aggregation for Federated Learning on User-Held Data https://arxiv.org/abs/1611.04482

• Practical Secure Aggregation for Privacy-Preserving Machine Learning https://storage.googleapis.com/pub-tools-public-publication-data/pdf/ae87385258d90b9e48377ed49d83d467b45d5776.pdf

• A Hybrid Approach to Privacy-Preserving Federated Learning https://arxiv.org/abs/1812.03224

• Analyzing Federated Learning through an Adversarial Lens https://arxiv.org/pdf/1811.12470

• How To Backdoor Federated Learning https://arxiv.org/abs/1807.00459

• 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

• Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning https://arxiv.org/pdf/1812.00535

• Exploiting Unintended Feature Leakage in Collaborative Learning https://arxiv.org/abs/1805.04049

• Analyzing Federated Learning through an Adversarial Lens https://arxiv.org/abs/1811.12470

• Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning https://arxiv.org/abs/1702.07464

• Protection Against Reconstruction and Its Applications in Private Federated Learning https://arxiv.org/pdf/1812.00984

• Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing https://arxiv.org/abs/1907.10218

• Differentially Private Data Generative Models https://arxiv.org/pdf/1812.02274

• Differentially Private Federated Learning: A Client Level Perspective https://arxiv.org/abs/1712.07557

• Privacy-Preserving Collaborative Deep Learning with Unreliable Participants https://arxiv.org/abs/1812.10113

• Scalable Private Learning with PATE https://arxiv.org/abs/1802.08908

• 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/

• Deep Leakage from Gradients http://papers.nips.cc/paper/9617-deep-leakage-from-gradients.pdf

• Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning https://arxiv.org/abs/1805.05838

System and Application

• Flower: A Friendly Federated Learning Framework https://arxiv.org/pdf/2007.14390.pdf

• Learning Private Neural Language Modeling with Attentive Aggregation https://arxiv.org/pdf/1812.07108

• Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning https://arxiv.org/abs/2003.09603

• Decentralized Knowledge Acquisition for Mobile Internet Applications https://link.springer.com/article/10.1007/s11280-019-00775-w

• A generic framework for privacy preserving deep learning https://arxiv.org/pdf/1811.04017.pdf

• Federated Learning of N-gram Language Models https://arxiv.org/pdf/1910.03432.pdf

• Towards Federated Learning at Scale: System Design https://arxiv.org/pdf/1902.01046.pdf

• Federated Learning for Keyword Spotting https://arxiv.org/abs/1810.05512

• Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data https://arxiv.org/abs/1810.08553

• Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System https://arxiv.org/pdf/1901.09888

• Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence https://arxiv.org/abs/1910.02109

• Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platform http://www.cs.ucf.edu/~mohaisen/doc/dsn19b.pdf

• Institutionally Distributed Deep Learning Networks https://arxiv.org/abs/1709.05929

• Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation https://arxiv.org/abs/1810.04304

• 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/

• Continuous Delivery for Machine Learning https://martinfowler.com/articles/cd4ml.html#EvolvingIntelligentSystemsWithoutBias

• Ease.ml/ci & Ease.ml/meter Towards Data Management for Statistical Generialization http://ease.ml/

• 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

• Federated Optimization in Heterogeneous Networks https://arxiv.org/abs/1812.06127

Un-org

• Federated Visual Classification with Real-World Data Distribution https://arxiv.org/abs/2003.08082

• Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification https://arxiv.org/abs/1909.06335

• LEAF: A Benchmark for Federated Settings https://arxiv.org/abs/1812.01097

• On the Convergence of FedAvg on Non-IID Data https://arxiv.org/abs/1907.02189

• Privacy-preserving Federated Brain Tumour Segmentation. https://arxiv.org/pdf/1910.00962.pdf

• ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries https://www.media.mit.edu/publications/ExpertMatcher/

• 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/

• Split Learning: Distributed and collaborative learning https://aiforsocialgood.github.io/iclr2019/accepted/track1/pdfs/31_aisg_iclr2019.pdf

• Asynchronous Federated Optimization https://arxiv.org/pdf/1903.03934

• Robust and Communication-Efficient Federated Learning from Non-IID Data https://arxiv.org/pdf/1903.02891

• One-Shot Federated Learning https://arxiv.org/pdf/1902.11175

• High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions https://arxiv.org/pdf/1902.08999

• Agnostic Federated Learning https://arxiv.org/pdf/1902.00146%C2%A0

• Peer-to-peer Federated Learning on Graphs https://arxiv.org/pdf/1901.11173

• SecureBoost: A Lossless Federated Learning Framework https://arxiv.org/pdf/1901.08755

• Federated Reinforcement Learning https://arxiv.org/pdf/1901.08277

• Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems https://arxiv.org/pdf/1901.06455

• Federated Learning via Over-the-Air Computation https://arxiv.org/pdf/1812.11750

• Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version) https://arxiv.org/pdf/1812.11494

• Multi-objective Evolutionary Federated Learning https://arxiv.org/pdf/1812.07478

• Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach https://arxiv.org/pdf/1812.03633

• A Hybrid Approach to Privacy-Preserving Federated Learning https://arxiv.org/pdf/1812.03224

• Applied Federated Learning: Improving Google Keyboard Query Suggestions https://arxiv.org/pdf/1812.02903

• Differentially Private Data Generative Models https://arxiv.org/pdf/1812.02274

• Protection Against Reconstruction and Its Applications in Private Federated Learning https://arxiv.org/pdf/1812.00984

• Split learning for health: Distributed deep learning without sharing raw patient data https://arxiv.org/pdf/1812.00564

• Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning https://arxiv.org/pdf/1812.00535

• LoAdaBoost:Loss-Based AdaBoost Federated Machine Learning on medical Data https://arxiv.org/pdf/1811.12629

• Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data https://arxiv.org/pdf/1811.11479

• Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning https://arxiv.org/pdf/1811.09904

• Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting https://arxiv.org/pdf/1811.09712

• Federated Learning Approach for Mobile Packet Classification https://arxiv.org/abs/1907.13113

• Collaborative Learning on the Edges: A Case Study on Connected Vehicles https://www.usenix.org/conference/hotedge19/presentation/lu

• Federated Learning for Time Series Forecasting Using Hybrid Model http://www.diva-portal.se/smash/get/diva2:1334629/FULLTEXT01.pdf

• Federated Learning: Challenges, Methods, and Future Directions https://arxiv.org/pdf/1908.07873.pdf

• Federated Learning with Matched Averaging https://openreview.net/forum?id=BkluqlSFDS

Code

• Flower https://flower.dev/

• PySyft https://github.com/OpenMined/PySyft

• Tensorflow Federated https://www.tensorflow.org/federated

• CrypTen https://github.com/facebookresearch/CrypTen

• FATE https://fate.fedai.org/

• DVC https://dvc.org/

• LEAF https://leaf.cmu.edu/

• Federated iNaturalist/Landmarkds https://github.com/google-research/google-research/tree/master/federated_vision_datasets

• FedML: A Research Library and Benchmark for Federated Machine Learning https://github.com/FedML-AI/FedML

Use-cases

MIT CSAIL/Harvard Medical/Tsinghua University’s Academy of Arts and Design

• https://arxiv.org/ftp/arxiv/papers/1903/1903.09296.pdf
• https://venturebeat.com/2019/03/25/federated-learning-technique-predicts-hospital-stay-and-patient-mortality/

Microsoft research/University of Chinese Academy of Sciences, Beijing, China

• https://arxiv.org/pdf/1907.09173.pdf

Boston University/Massachusetts General Hospital

• https://www.ncbi.nlm.nih.gov/pubmed/29500022

Google

• https://ai.googleblog.com/2017/04/federated-learning-collaborative.html
• https://www.statnews.com/2019/09/10/google-mayo-clinic-partnership-patient-data/

Tencent WeBank

• https://www.digfingroup.com/webank-clustar/

Nvidia/King’s College London, American College of Radiology, MGH and BWH Center for Clinical Data Science, and UCLA Health... etc

• https://venturebeat.com/2019/10/13/nvidia-uses-federated-learning-to-create-medical-imaging-ai/
• https://blogs.nvidia.com/blog/2019/12/01/clara-federated-learning/

Company

• Adap https://adap.com/en

• Snips

  • https://snips.ai/
  • https://www.theverge.com/2019/11/21/20975607/sonos-buys-snips-ai-voice-assistant-privacy

• Privacy.ai https://privacy.ai/

• OpenMined https://www.openmined.org/

• Arkhn https://arkhn.org/en/

• Scaleout https://scaleoutsystems.com/

• MELLODDY https://www.melloddy.eu/

• DataFleets https://www.datafleets.com/