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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
Last synced: about 1 month ago
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A curated list of resources dedicated to federated learning.
- Host: GitHub
- URL: https://github.com/timmers/awesome-federated-learning
- Owner: timmers
- Created: 2019-03-14T02:34:11.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-03-18T13:46:13.000Z (over 2 years ago)
- Last Synced: 2024-05-19T17:45:01.157Z (7 months ago)
- Size: 11.7 KB
- Stars: 99
- Watchers: 5
- Forks: 24
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
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/2007.14390Original paper: Communication-Efficient Learning of Deep Networks from Decentralized Data
https://arxiv.org/abs/1602.05629Asynchronous Federated Optimization
https://arxiv.org/pdf/1903.03934Towards Federated Learning at Scale: System Design
https://arxiv.org/pdf/1902.01046Robust and Communication-Efficient Federated Learning from Non-IID Data
https://arxiv.org/pdf/1903.02891One-Shot Federated Learning
https://arxiv.org/pdf/1902.11175High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions
https://arxiv.org/pdf/1902.08999Federated Machine Learning: Concept and Applications
https://arxiv.org/pdf/1902.04885Agnostic Federated Learning
https://arxiv.org/pdf/1902.00146ÂPeer-to-peer Federated Learning on Graphs
https://arxiv.org/pdf/1901.11173Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System
https://arxiv.org/pdf/1901.09888SecureBoost: A Lossless Federated Learning Framework
https://arxiv.org/pdf/1901.08755Federated Reinforcement Learning
https://arxiv.org/pdf/1901.08277Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
https://arxiv.org/pdf/1901.06455Federated Learning via Over-the-Air Computation
https://arxiv.org/pdf/1812.11750Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)
https://arxiv.org/pdf/1812.11494Multi-objective Evolutionary Federated Learning
https://arxiv.org/pdf/1812.07478Federated Optimization for Heterogeneous Networks
https://arxiv.org/pdf/1812.06127Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach
https://arxiv.org/pdf/1812.03633No Peek: A Survey of private distributed deep learning
https://arxiv.org/pdf/1812.03288A Hybrid Approach to Privacy-Preserving Federated Learning
https://arxiv.org/pdf/1812.03224Applied Federated Learning: Improving Google Keyboard Query Suggestions
https://arxiv.org/pdf/1812.02903Split learning for health: Distributed deep learning without sharing raw patient data
https://arxiv.org/pdf/1812.00564LoAdaBoost:Loss-Based AdaBoost Federated Machine Learning on medical Data
https://arxiv.org/pdf/1811.12629Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data
https://arxiv.org/pdf/1811.11479Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning
https://arxiv.org/pdf/1811.09904Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting
https://arxiv.org/pdf/1811.09712A Federated Learning Approach for Mobile Packet Classification
https://arxiv.org/abs/1907.13113Collaborative Learning on the Edges: A Case Study on Connected Vehicles
https://www.usenix.org/conference/hotedge19/presentation/luFederated Learning for Time Series Forecasting Using Hybrid Model
http://www.diva-portal.se/smash/get/diva2:1334629/FULLTEXT01.pdfFederated Learning: Challenges, Methods, and Future Directions
https://arxiv.org/pdf/1908.07873.pdfAsymmetrically Vertical Federated Learning
https://arxiv.org/abs/1808.03949On the Design of Communication Efficient Federated Learning over Wireless Networks
https://arxiv.org/abs/2004.07351Secure Federated Learning in 5G Mobile Networks
https://arxiv.org/abs/2004.06700Differentially Private AirComp Federated Learning with Power Adaptation Harnessing Receiver Noise
https://arxiv.org/abs/2004.06337Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface
https://arxiv.org/abs/2004.05843Towards 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/1812.00910Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning
https://arxiv.org/pdf/1812.00535Exploiting Unintended Feature Leakage in Collaborative Learning
https://arxiv.org/abs/1805.04049Analyzing Federated Learning through an Adversarial Lens
https://arxiv.org/abs/1811.12470Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
https://arxiv.org/abs/1702.07464Protection Against Reconstruction and Its Applications in Private Federated Learning
https://arxiv.org/pdf/1812.00984Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing
https://arxiv.org/abs/1907.10218How To Backdoor Federated Learning
https://arxiv.org/abs/1807.00459Differentially Private Data Generative Models
https://arxiv.org/pdf/1812.02274An 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/PySyftTensorflow Federated
https://www.tensorflow.org/federatedCrypTen
https://github.com/facebookresearch/CrypTenFATE
https://fate.fedai.org/DVC
https://dvc.org/LEAF
https://leaf.cmu.edu/