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https://github.com/timmers/awesome-federated-learning

A curated list of resources dedicated to federated learning.
https://github.com/timmers/awesome-federated-learning

List: awesome-federated-learning

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A curated list of resources dedicated to federated learning.

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README 

        
# awesome-federated-learning [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)

📚 A curated collection of research papers, codes, tutorials and blogs on Federated Computing/Learning.

# Contributing

Please feel free to [pull requests](https://github.com/timmers/awesome-federated-learning/pulls)

# Papers:



Flower: A Friendly Federated Learning Platform 

https://arxiv.org/abs/2007.14390



Original paper: Communication-Efficient Learning of Deep Networks from Decentralized Data

https://arxiv.org/abs/1602.05629



Asynchronous Federated Optimization 

https://arxiv.org/pdf/1903.03934



Towards Federated Learning at Scale: System Design 

https://arxiv.org/pdf/1902.01046



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



Federated Machine Learning: Concept and Applications 

https://arxiv.org/pdf/1902.04885



Agnostic Federated Learning

https://arxiv.org/pdf/1902.00146 



Peer-to-peer Federated Learning on Graphs 

https://arxiv.org/pdf/1901.11173



Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System 

https://arxiv.org/pdf/1901.09888



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



Federated Optimization for Heterogeneous Networks 

https://arxiv.org/pdf/1812.06127



Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach 

https://arxiv.org/pdf/1812.03633



No Peek: A Survey of private distributed deep learning 

https://arxiv.org/pdf/1812.03288



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



Split learning for health: Distributed deep learning without sharing raw patient data 

https://arxiv.org/pdf/1812.00564



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



A 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



Asymmetrically Vertical Federated Learning

https://arxiv.org/abs/1808.03949



On the Design of Communication Efficient Federated Learning over Wireless Networks

https://arxiv.org/abs/2004.07351



Secure Federated Learning in 5G Mobile Networks

https://arxiv.org/abs/2004.06700



Differentially Private AirComp Federated Learning with Power Adaptation Harnessing Receiver Noise

https://arxiv.org/abs/2004.06337



Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface

https://arxiv.org/abs/2004.05843



Towards Realistic Byzantine-Robust Federated Learning

https://arxiv.org/abs/2004.04986



## Blockchained Federated Learning:



Blockchained On-Device Federated Learning

https://arxiv.org/abs/1808.03949



## Adversarial Federated Learning (attacks and defenses):



Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks

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



How To Backdoor Federated Learning

https://arxiv.org/abs/1807.00459



Differentially Private Data Generative Models 

https://arxiv.org/pdf/1812.02274



An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies

https://arxiv.org/abs/2004.04676



## Code/Frameworks:



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/