Client Selection

Client selection in federated learning (FL) aims to optimize the participation of distributed clients in model training, balancing model accuracy, communication efficiency, and fairness. Current research focuses on developing sophisticated algorithms, often employing techniques like submodular maximization, bilevel optimization, and generative models, to select clients based on various criteria including data quality, resource availability, and privacy constraints. These advancements are crucial for improving the efficiency and robustness of FL, enabling its wider adoption in privacy-sensitive applications like healthcare and finance.

Papers

July 10, 2022

FedSS: Federated Learning with Smart Selection of clients
Ammar Tahir, Yongzhou Chen, Prashanti Nilayam
Client Selection Selection Policy Client Collaboration Faster Client

July 1, 2022

Better Methods and Theory for Federated Learning: Compression, Client Selection and Heterogeneity
Samuel Horváth
Optimization Purpose Linear Compression Client Selection Current Method Centralized Data

May 11, 2022

Blockchain-based Secure Client Selection in Federated Learning
Truc Nguyen, Phuc Thai, Tre' R. Jeter, Thang N. Dinh, My T. Thai
Secure Aggregation Client Selection Blockchain Transaction

January 15, 2022

Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selection
Guangyuan Shen, Dehong Gao, Libin Yang, Fang Zhou, Duanxiao Song, Wei Lou, Shirui Pan
Heterogeneous Federated Learning Client Selection Approximate Sampling Sample Allocation

January 9, 2022

A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning
Sai Qian Zhang, Jieyu Lin, Qi Zhang
Multi Agent Reinforcement Learning Federated Learning Client Selection Federated Reinforcement Learning

December 2, 2021

Context-Aware Online Client Selection for Hierarchical Federated Learning
Zhe Qu, Rui Duan, Lixing Chen, Jie Xu, Zhuo Lu, Yao Liu
Multi Armed Bandit Client Selection Hierarchical Federated Learning Edge Server

November 19, 2021

Client Selection

Papers

FedSS: Federated Learning with Smart Selection of clients

Better Methods and Theory for Federated Learning: Compression, Client Selection and Heterogeneity

Blockchain-based Secure Client Selection in Federated Learning

Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selection

A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning

Context-Aware Online Client Selection for Hierarchical Federated Learning

A Feedback Integrated Web-Based Multi-Criteria Group Decision Support Model for Contractor Selection using Fuzzy Analytic Hierarchy Process

Client Selection in Federated Learning based on Gradients Importance