awesome-federated-learning

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

Contributing

Please feel free to pull requests

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/