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

March 30, 2023

DPP-based Client Selection for Federated Learning with Non-IID Data
Yuxuan Zhang, Chao Xu, Howard H. Yang, Xijun Wang, Tony Q. S. Quek
Raw Data Convergence Rate Client Selection Participant Selection

March 18, 2023

Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach
Dan Ben Ami, Kobi Cohen, Qing Zhao
Machine Learning Strong Generalization Federated Learning Multi Armed Bandit Bandit Algorithm Client Selection Logarithmic Regret

February 17, 2023

Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspective
Yash Travadi, Le Peng, Xuan Bi, Ju Sun, Mochen Yang
Client Selection Incentive Mechanism Social Welfare Privacy Preserving Learning Dynamic Fairness

December 24, 2022

When Do Curricula Work in Federated Learning?
Saeed Vahidian, Sreevatsank Kadaveru, Woonjoon Baek, Weijia Wang, Vyacheslav Kungurtsev, Chen Chen, Mubarak Shah, Bill Lin
Data Heterogeneity Sequential Learning Client Selection Local SGD Biased Gradient

December 19, 2022

Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning
Zhida Jiang, Yang Xu, Hongli Xu, Zhiyuan Wang, Chen Qian
Federated Learning Heterogeneous Federated Learning Client Selection Adaptive Control Gradient Compression

December 11, 2022

Client Selection for Federated Bayesian Learning
Jiarong Yang, Yuan Liu, Rahif Kassab
Client Selection Bayesian Learning Model Convergence Local Learning Stein Variational Gradient Descent Stein Discrepancy

November 25, 2022

MDA: Availability-Aware Federated Learning Client Selection
Amin Eslami Abyane, Steve Drew, Hadi Hemmati
Client Selection Federated Learning Client Faster Client

November 3, 2022

Client Selection in Federated Learning: Principles, Challenges, and Opportunities
Lei Fu, Huanle Zhang, Ge Gao, Mi Zhang, Xin Liu
Technical Challenge Emerging Opportunity General Principle Client Selection Heterogeneous Client

October 31, 2022

FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices
Osama Wehbi, Sarhad Arisdakessian, Omar Abdel Wahab, Hadi Otrok, Safa Otoum, Azzam Mourad, Mohsen Guizani
Client Selection Random Selection Strategic Cost Selection

September 27, 2022

A Snapshot of the Frontiers of Client Selection in Federated Learning
Gergely Dániel Németh, Miguel Ángel Lozano, Novi Quadrianto, Nuria Oliver
Client Selection New Frontier Single Snapshot Federated Learning Architecture

August 19, 2022

Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning
Zachary Charles, Kallista Bonawitz, Stanislav Chiknavaryan, Brendan McMahan, Blaise Agüera y Arcas
Federated Learning Privacy Preserving Timely Communication Client Selection Negative Primitive

August 10, 2022

Fast Heterogeneous Federated Learning with Hybrid Client Selection
Guangyuan Shen, Dehong Gao, Duanxiao Song, Libin Yang, Xukai Zhou, Shirui Pan, Wei Lou, Fang Zhou
Variance Reduction Heterogeneous Federated Learning Client Selection Cluster Specific Random Effect

August 8, 2022

Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets
Zhipeng Cheng, Xuwei Fan, Minghui Liwang, Ning Chen, Xianbin Wang
Federated Learning Multi Agent Wireless Network Client Selection Budget Constraint Non Cooperative Game Deep Reinforcement Learning Algorithm

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

A Feedback Integrated Web-Based Multi-Criteria Group Decision Support Model for Contractor Selection using Fuzzy Analytic Hierarchy Process
Abimbola Helen Afolayan, Bolanle Adefowoke Ojokoh, Adebayo Adetunmbi
Human Feedback Client Selection PAPER Demand \Cite{meir2021market Multi Criterion Feedback Mechanism Group Decision

Client Selection

Papers

DPP-based Client Selection for Federated Learning with Non-IID Data

Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach

Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspective

When Do Curricula Work in Federated Learning?

Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning

Client Selection for Federated Bayesian Learning

MDA: Availability-Aware Federated Learning Client Selection

Client Selection in Federated Learning: Principles, Challenges, and Opportunities

FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices

A Snapshot of the Frontiers of Client Selection in Federated Learning

Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning

Fast Heterogeneous Federated Learning with Hybrid Client Selection

Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets

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