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

November 9, 2024

Personalized Hierarchical Split Federated Learning in Wireless Networks
Md-Ferdous Pervej, Andreas F. Molisch
Machine Learning Model Wireless Network Client Selection Personalized Model Large Scale Machine Learning

November 2, 2024

Optimizing Federated Learning by Entropy-Based Client Selection
Andreas Lutz, Gabriele Steidl, Karsten Müller, Wojciech Samek
Deep Learning Federated Learning Label Distribution Client Selection Federated Automatic Differentiation

November 1, 2024

BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networks
Sushilkumar Yadav, Irem Bor-Yaliniz
Client Selection Healthcare Application Bias Challenge

October 8, 2024

Multi-Session Client-Centered Treatment Outcome Evaluation in Psychotherapy
Hongbin Na, Tao Shen, Shumao Yu, Ling Chen
Client Selection Psychotherapy Model Special Session Client Centered Assessment

October 7, 2024

Aiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information
Emanuel Buttaci, Giuseppe Carlo Calafiore
Global Convergence Client Selection Gradient Based Optimization Additive Perturbation FedAvg Algorithm

September 16, 2024

A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approach
Maryam Ben Driss, Essaid Sabir, Halima Elbiaze, Abdoulaye Baniré Diallo, Mohamed Sadik
Client Selection Air Federated Learning Grey Wolf

September 9, 2024

CoBo: Collaborative Learning via Bilevel Optimization
Diba Hashemi, Lie He, Martin Jaggi
Bilevel Optimization Optimization Problem Model Training Collaborative Learning Client Selection

August 24, 2024

Submodular Maximization Approaches for Equitable Client Selection in Federated Learning
Andrés Catalino Castillo Jiménez, Ege C. Kaya, Lintao Ye, Abolfazl Hashemi
Federated Learning Submodular Maximization Client Selection Client Heterogeneity

August 16, 2024

The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy
Jiating Ma, Yipeng Zhou, Qi Li, Quan Z. Sheng, Laizhong Cui, Jiangchuan Liu
Absolute Stance Bias Real Power Client Selection Convex Loss Non Convex Loss Function Heterogeneous Privacy

June 18, 2024

SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learning
Qiugang Zhan, Jinbo Cao, Xiurui Xie, Malu Zhang, Huajin Tang, Guisong Liu
Federated Learning Client Selection Data Distribution Client Clustering Consumer Credit Global Sampling

June 3, 2024

May 25, 2024

Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detection
M. Saeid HaghighiFard, Sinem Coleri
Anomaly Detection Client Selection Vehicular Network Hierarchical Federated Learning Robust Anomaly Detection

May 22, 2024

Emulating Full Client Participation: A Long-Term Client Selection Strategy for Federated Learning
Qingming Li, Juzheng Miao, Puning Zhao, Li Zhou, Shouling Ji, Bowen Zhou, Furui Liu
Client Selection Convergence Performance Client Participation

May 21, 2024

Maverick-Aware Shapley Valuation for Client Selection in Federated Learning
Mengwei Yang, Ismat Jarin, Baturalp Buyukates, Salman Avestimehr, Athina Markopoulou
Shapley Value Client Selection Contribution Evaluation

May 10, 2024

FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization
Zhiyuan Ning, Chunlin Tian, Meng Xiao, Wei Fan, Pengyang Wang, Li Li, Pengfei Wang, Yuanchun Zhou
Generative Framework Gradient Based Optimization Client Selection

May 7, 2024

Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning
Chunlin Tian, Zhan Shi, Xinpeng Qin, Li Li, Chengzhong Xu
Federated Learning Imitation Learning Client Selection Pairwise Learning Training Convergence Device Selection

March 30, 2024

From Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selection
Jingwen Tong, Zhenzhen Chen, Liqun Fu, Jun Zhang, Zhu Han
LeArning Abstract Multi Armed Bandit Model Performance Closed Loop Client Selection Data Analytics Prediction Framework

March 26, 2024

GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learning
Shijie Na, Yuzhi Liang, Siu-Ming Yiu
Federated Learning Natural Gradient Net Present Value Client Selection Cifar 10 Federated Learning Client

February 13, 2024

FLASH: Federated Learning Across Simultaneous Heterogeneities
Xiangyu Chang, Sk Miraj Ahmed, Srikanth V. Krishnamurthy, Basak Guler, Ananthram Swami, Samet Oymak, Amit K. Roy-Chowdhury
Client Selection Client Heterogeneity Linear Time Algorithm Unknown Heterogeneity Latency Critical

Client Selection

Papers

Personalized Hierarchical Split Federated Learning in Wireless Networks

Optimizing Federated Learning by Entropy-Based Client Selection

BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networks

Multi-Session Client-Centered Treatment Outcome Evaluation in Psychotherapy

Aiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information

A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approach

CoBo: Collaborative Learning via Bilevel Optimization

Submodular Maximization Approaches for Equitable Client Selection in Federated Learning

The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy

SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learning

Efficient Data Distribution Estimation for Accelerated Federated Learning

Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning

Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detection

Emulating Full Client Participation: A Long-Term Client Selection Strategy for Federated Learning

Maverick-Aware Shapley Valuation for Client Selection in Federated Learning

FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization

Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning

From Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selection

GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learning

FLASH: Federated Learning Across Simultaneous Heterogeneities