📚 A curated collection of research papers, codes, tutorials and blogs on Federated Computing/Learning.
Please feel free to pull requests
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 On-Device Federated Learning https://arxiv.org/abs/1808.03949
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
Flower https://flower.dev/
PySyft https://github.com/OpenMined/PySyft
Tensorflow Federated https://www.tensorflow.org/federated
CrypTen https://github.com/facebookresearch/CrypTen
DVC https://dvc.org/