Asynchronous Federated Learning

Asynchronous federated learning (AFL) aims to improve the efficiency and scalability of federated learning by allowing clients to update a shared model independently and at their own pace, rather than synchronously. Current research focuses on addressing challenges like staleness of updates, heterogeneity in client resources and data distributions, and robustness against Byzantine failures, often employing techniques such as buffered aggregation, gradient compression, and adaptive client selection. These advancements enhance the practicality of federated learning for resource-constrained and decentralized environments, with applications ranging from IoT networks and autonomous driving to geo-distributed systems and satellite constellations.

Papers

September 30, 2024

Quantized and Asynchronous Federated Learning
Tomas Ortega, Hamid Jafarkhani
Multiplier Free Quantization Asynchronous Federated Learning Asynchronous Execution

September 2, 2024

GAS: Generative Activation-Aided Asynchronous Split Federated Learning
Jiarong Yang, Yuan Liu
Generative Network Asynchronous Federated Learning Split Federated Learning Asynchronous Stochastic Gradient Descent

August 26, 2024

Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network
Shunfeng Chu, Jun Li, Jianxin Wang, Yiyang Ni, Kang Wei, Wen Chen, Shi Jin
Digital Twin Internet of Thing Network Asynchronous Federated Learning Device Scheduling Device Selection

July 28, 2024

Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchain
Weiliang Chen, Li Jia, Yang Zhou, Qianqian Ren
Trajectory Prediction Blockchain Based Platform Asynchronous Federated Learning Autonomous Driving Application Vehicle Data Reputation System

July 6, 2024

A Joint Approach to Local Updating and Gradient Compression for Efficient Asynchronous Federated Learning
Jiajun Song, Jiajun Luo, Rongwei Lu, Shuzhao Xie, Bin Chen, Zhi Wang
Asynchronous Federated Learning Gradient Compression Local Update

June 19, 2024

June 5, 2024

FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting
Jeffrey Ma, Alan Tu, Yiling Chen, Vijay Janapa Reddi
Convergence Guarantee Asynchronous Federated Learning Fairness Aware Aggregation Staleness Problem

June 3, 2024

June 1, 2024

FedAST: Federated Asynchronous Simultaneous Training
Baris Askin, Pranay Sharma, Carlee Joe-Wong, Gauri Joshi
Asynchronous Federated Learning Simultaneous Learning Simultaneous Training Asynchronous Aggregation

April 20, 2024

PAFedFV: Personalized and Asynchronous Federated Learning for Finger Vein Recognition
Hengyu Mu, Jian Guo, Chong Han, Lijuan Sun
Asynchronous Federated Learning Finger Vein Recognition Palm Vein

April 19, 2024

CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance
Zeke Xia, Ming Hu, Dengke Yan, Xiaofei Xie, Tianlin Li, Anran Li, Junlong Zhou, Mingsong Chen
Asynchronous Federated Learning Machine Learning Paradigm Artificial Intelligence of Thing Feature Impact

April 12, 2024

Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing
Cui Zhang, Xiao Xu, Qiong Wu, Pingyi Fan, Qiang Fan, Huiling Zhu, Jiangzhou Wang
Deep Reinforcement Learning Vehicle Plan Asynchronous Federated Learning Byzantine Attack Vehicle Data Global Aggregation Vehicular Edge Computing Vehicle Edge Computing

March 3, 2024

Asyn2F: An Asynchronous Federated Learning Framework with Bidirectional Model Aggregation
Tien-Dung Cao, Nguyen T. Vuong, Thai Q. Le, Hoang V. N. Dao, Tram Truong-Huu
Model Aggregation Asynchronous Federated Learning Five Dollar Model

February 17, 2024

Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients
Xiaolu Wang, Zijian Li, Shi Jin, Jun Zhang
Federated Averaging Asynchronous Federated Learning Linear Speedup Heterogeneous Client FedAvg Converges Stale Global Model

February 16, 2024

Enhancing Convergence in Federated Learning: A Contribution-Aware Asynchronous Approach
Changxin Xu, Yuxin Qiao, Zhanxin Zhou, Fanghao Ni, Jize Xiong
Early Stage Convergence Federated Averaging Asynchronous Federated Learning Asynchronous Approach Stale Global Model

February 14, 2024

Momentum Approximation in Asynchronous Private Federated Learning
Tao Yu, Congzheng Song, Jianyu Wang, Mona Chitnis
Federated Learning Asynchronous Federated Learning Asynchronous Communication Momentum Based Method

February 12, 2024

Queuing dynamics of asynchronous Federated Learning
Louis Leconte, Matthieu Jonckheere, Sergey Samsonov, Eric Moulines
Asynchronous Federated Learning Asynchronous Algorithm Gradient Delay Structured Network

January 24, 2024

Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems
Jikun Gao, Ioannis Mavromatis, Peizheng Li, Pietro Carnelli, Aftab Khan
Global Model Model Aggregation Asynchronous Federated Learning

Asynchronous Federated Learning

Papers

Quantized and Asynchronous Federated Learning

GAS: Generative Activation-Aided Asynchronous Split Federated Learning

Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network

Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchain

A Joint Approach to Local Updating and Gradient Compression for Efficient Asynchronous Federated Learning

DRACO: Decentralized Asynchronous Federated Learning over Continuous Row-Stochastic Network Matrices

A Resource-Adaptive Approach for Federated Learning under Resource-Constrained Environments

FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting

Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients

Asynchronous Byzantine Federated Learning

FedAST: Federated Asynchronous Simultaneous Training

PAFedFV: Personalized and Asynchronous Federated Learning for Finger Vein Recognition

CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance

Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing

Asyn2F: An Asynchronous Federated Learning Framework with Bidirectional Model Aggregation

Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients

Enhancing Convergence in Federated Learning: A Contribution-Aware Asynchronous Approach

Momentum Approximation in Asynchronous Private Federated Learning

Queuing dynamics of asynchronous Federated Learning

Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems