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, 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 Unknown Heterogeneity Linear Time Algorithm Latency Critical

January 30, 2024

Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection
Liangqi Yuan, Dong-Jun Han, Su Wang, Devesh Upadhyay, Christopher G. Brinton
Federated Learning Multimodal Phenomenon Client Selection Modality Model

January 10, 2024

Strategic Client Selection to Address Non-IIDness in HAPS-enabled FL Networks
Amin Farajzadeh, Animesh Yadav, Halim Yanikomeroglu
Client Selection Data Distribution Heterogeneous Network Large Network High Altitude Platform

January 6, 2024

Distributed client selection with multi-objective in federated learning assisted Internet of Vehicles
Narisu Cha, Long Chang
LeArning Abstract Vehicle Plan Client Selection Fuzzy Logic Machine Learning Framework

December 23, 2023

Personalized Federated Learning with Attention-based Client Selection
Zihan Chen, Jundong Li, Cong Shen
Personalized Federated Learning Client Selection

December 22, 2023

Balancing Energy Efficiency and Distributional Robustness in Over-the-Air Federated Learning
Mohamed Badi, Chaouki Ben Issaid, Anis Elgabli, Mehdi Bennis
Native Robustness Energy Efficiency Client Selection Air Federated Learning Distributional Robustness Mobile Edge

December 14, 2023

Greedy Shapley Client Selection for Communication-Efficient Federated Learning
Pranava Singhal, Shashi Raj Pandey, Petar Popovski
Client Selection

December 5, 2023

Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service
Meiying Zhang, Huan Zhao, Sheldon Ebron, Ruitao Xie, Kan Yang
Federated Learning Task Scheduling Client Selection Client Heterogeneity

November 28, 2023

Asynchronous Wireless Federated Learning with Probabilistic Client Selection
Jiarong Yang, Yuan Liu, Fangjiong Chen, Wen Chen, Changle Li
Federated Learning Convergence Rate Client Selection Asynchronous Federated Learning

November 12, 2023

A Comprehensive Survey On Client Selections in Federated Learning
Ala Gouissem, Zina Chkirbene, Ridha Hamila
Comprehensive Survey Wireless Network Client Selection Selection Policy Heterogeneous Network

October 27, 2023

CEFL: Carbon-Efficient Federated Learning
Talha Mehboob, Noman Bashir, Jesus Omana Iglesias, Michael Zink, David Irwin
Federated Learning Client Selection Communication Efficient Federated Learning

October 1, 2023

Intelligent Client Selection for Federated Learning using Cellular Automata
Nikolaos Pavlidis, Vasileios Perifanis, Theodoros Panagiotis Chatzinikolaou, Georgios Ch. Sirakoulis, Pavlos S. Efraimidis
Client Selection Cellular Automaton

September 30, 2023

Accelerating Non-IID Federated Learning via Heterogeneity-Guided Client Sampling
Huancheng Chen, Haris Vikalo
Client Selection Statistical Heterogeneity Client Sampling Federated Learning Client Based Sampling

September 25, 2023

Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey
Alessandro Fabris, Nina Baranowska, Matthew J. Dennis, David Graus, Philipp Hacker, Jorge Saldivar, Frederik Zuiderveen Borgesius, Asia J. Biega
Procedural Fairness Absolute Stance Bias Client Selection Group DIscrimination Interdisciplinary Perspective

July 25, 2023

Blockchain-based Optimized Client Selection and Privacy Preserved Framework for Federated Learning
Attia Qammar, Abdenacer Naouri, Jianguo Ding, Huansheng Ning
New Framework Privacy Preserving Privacy Policy Homomorphic Encryption Smart Contract Client Selection

July 17, 2023

FedCME: Client Matching and Classifier Exchanging to Handle Data Heterogeneity in Federated Learning
Jun Nie, Danyang Xiao, Lei Yang, Weigang Wu
Data Heterogeneity Client Selection Model Convergence Local Training Classifier Exchanging

July 14, 2023

Ed-Fed: A generic federated learning framework with resource-aware client selection for edge devices
Zitha Sasindran, Harsha Yelchuri, T. V. Prabhakar
Automatic Speech Recognition Client Selection Learning Framework

July 10, 2023

FedDCT: A Dynamic Cross-Tier Federated Learning Framework in Wireless Networks
Youquan Xian, Xiaoyun Gan, Chuanjian Yao, Dongcheng Li, Peng Wang, Peng Liu, Ying Zhao
Client Selection

Client Selection

Papers

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

Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection

Strategic Client Selection to Address Non-IIDness in HAPS-enabled FL Networks

Distributed client selection with multi-objective in federated learning assisted Internet of Vehicles

Personalized Federated Learning with Attention-based Client Selection

Balancing Energy Efficiency and Distributional Robustness in Over-the-Air Federated Learning

Greedy Shapley Client Selection for Communication-Efficient Federated Learning

Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service

Asynchronous Wireless Federated Learning with Probabilistic Client Selection

A Comprehensive Survey On Client Selections in Federated Learning

CEFL: Carbon-Efficient Federated Learning

Intelligent Client Selection for Federated Learning using Cellular Automata

Accelerating Non-IID Federated Learning via Heterogeneity-Guided Client Sampling

Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey

Blockchain-based Optimized Client Selection and Privacy Preserved Framework for Federated Learning

FedCME: Client Matching and Classifier Exchanging to Handle Data Heterogeneity in Federated Learning

Ed-Fed: A generic federated learning framework with resource-aware client selection for edge devices

FedDCT: A Dynamic Cross-Tier Federated Learning Framework in Wireless Networks