Collect some Asynchronous Federated Learning papers.
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If you find some overlooked papers, please open issues or pull requests(recommended), following the Contributing
section.
Last Update: Jan 19, 2023 20:20:30
- [FedAvg] Communication-Efficient Learning of Deep Networks from Decentralized Data(AISTAT) [PDF]
- [FedML] FedML: A Research Library and Benchmark for Federated Machine Learning(arXiv) [Home] [PDF] [GitHub] [Docs]
- [FedHF] FedHF: 🔨 A Flexible Federated Learning Simulator. [GitHub]
- [FederatedScope] FederatedScope: A Flexible Federated Learning Platform for Heterogeneity [Home] [GitHub] [PDF]
- [PySyft] PySyft: A Library for Easy Federated Learning(Studies in Computational Intelligence) [GitHub] [PDF]
- [FedLab] FedLab: A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research. [GitHub] [Docs]
- [Open Problem] Advances and Open Problems in Federated Learning(FnTML) [PDF]
- Asynchronous Federated Learning on Heterogeneous Devices: A Survey(arXiv) [PDF]
- [FedProx] Federated Optimization in Heterogeneous Networks(MLSys 2020) [PDF] [GitHub]
- [FedBN] FedBN: Federated Learning on Non-IID Features via Local Batch Normalization(ICLR 2021) [PDF] [GitHub]
- [Pisces] Pisces: Efficient Federated Learning via Guided Asynchronous Training(ACM SoCC 2022) [PDF] [GitHub]
[WIP]
- [TiFL] TiFL: A Tier-based Federated Learning System(HPDC 2020) [PDF]
- [FedAT] FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers(arXiv) [PDF]
- [AsyncFedED] AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregation(arXiv) [PDF]
- [FedSA] FedSA: A staleness-aware asynchronous Federated Learning algorithm with non-IID data(FGCS Elsevier) [PDF]
- [SAFA] SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead(IEEE Transactions on Computers) [PDF]
- [FedDR] FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization(ResearchGate) [PDF]
- [AFSGD-VP] Privacy-Preserving Asynchronous Vertical Federated Learning Algorithms for Multiparty Collaborative Learning(TNNLS) [PDF]
- An Asynchronous Federated Learning Approach for a Security Source Code Scanner(ICISSP 2021) [PDF]
- [FedConD] Asynchronous Federated Learning for Sensor Data with Concept Drift(arXiv) [PDF]
- [FedBuff] Federated Learning with Buffered Asynchronous Aggregation(arXiv) [PDF]
- Adaptive Task Allocation for Asynchronous Federated and Parallelized Mobile Edge Learning(arXiv) [PDF]
- [ASO-Fed] Asynchronous Online Federated Learning for Edge Devices with Non-IID Data(Big Data 2020) [PDF]
- [VAFL] VAFL: a Method of Vertical Asynchronous Federated Learning(ICML 2020) [PDF]
- [FedAsync] Asynchronous Federated Optimization(OPT 2020) [PDF]
- [DP-AFL] Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics(TII) [PDF]
- Asynchronous Federated Learning for Geospatial Applications(ECML PKDD 2018) [PDF]
- Federated learning for ultra-reliable low-latency V2V communications(GLOBECOM) [PDF]
[WIP]
[WIP]
You can contribute to this project by opening an issue or creating a pull request on GitHub.
Add paper to the papers.yaml
file with the following format:
- title: "Communication-Efficient Learning of Deep Networks from Decentralized Data"
abbr: FedAvg
year: 2016
conf: AISTAT
links:
PDF: https://arxiv.org/abs/1602.05629.pdf
GitHub:
@misc{awesomeafl,
title = {awesome-asyncrhonous-federated-learning},
author = {Bingjie Yan},
year = {2022},
howpublished = {\\url{https://github.com/beiyuouo/awesome-asynchronous-federated-learning}
}