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resources about federated learning and privacy in machine learning

Awesome Federated Learning Awesome

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

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Papers

Introduction & Survey

  • Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies https://ieeexplore.ieee.org/document/9780218

  • The Internet of Federated Things (IoFT) https://ieeexplore.ieee.org/document/9611259

  • 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

  • Federated Learning with Formal Differential Privacy Guarantees https://ai.googleblog.com/2022/02/federated-learning-with-formal.html

  • Applying Differential Privacy to Large Scale Image Classification https://ai.googleblog.com/2022/02/applying-differential-privacy-to-large.html

  • Towards Causal Federated Learning For Enhanced Robustness And Privacy https://arxiv.org/pdf/2104.06557.pdf ICLR DPML 2021

  • FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning https://arxiv.org/abs/2102.02514

  • OpenFL: An open-source framework for Federated Learning https://arxiv.org/abs/2105.06413

  • A Bayesian Federated Learning Framework with Multivariate Gaussian Product https://arxiv.org/abs/2102.01936

  • 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

  • Record and Reward Federated Learning Contributions with Blockchain https://mblocklab.com/RecordandReward.pdf

  • 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

  • FedProf: Optimizing Federated Learning with Dynamic Data Profiling https://arxiv.org/abs/2102.01733

  • FedBN: Federated Learning on Non-IID Features via Local Batch Normalization https://arxiv.org/abs/2102.07623

  • A Scalable Approach for Partially Local Federated Learning https://ai.googleblog.com/2021/12/a-scalable-approach-for-partially-local.html?m=1

  • 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

  • OpenFL: An open-source framework for Federated Learning - https://github.com/intel/openfl

  • 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

  • XayNet: Open source framework for federated learning in Rust https://xaynet.webflow.io/

  • EnvisEdge: https://github.com/NimbleEdge/EnvisEdge

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

  • integrate.ai https://integrate.ai

    • IntegrateFL: A SaaS platform for Federated Learning https://integrate.ai/integratefl/

  • 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/

  • Xayn AG https://www.xayn.com/

  • NimbleEdge https://www.nimbleedge.ai/

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