Client Selection in Federated Learning

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Overview

Federated learning trains a shared model across devices or institutions that keep their data local, so client selection determines whose updates shape the model in each round. Methods rank or group clients using signals such as gradient or loss, data diversity and uncertainty, device availability and compute cost, or expected contribution; optimization and reinforcement learning can adapt choices over time. Evaluations commonly use image benchmarks such as MNIST and CIFAR alongside edge, IoT, and clinical settings with non-IID data, stragglers, or limited participation. A central tension is that selecting informative and fast clients can speed training but exclude groups or distort the target population, while privacy limits how accurately their data and contributions can be measured.

Latest papers

CardsList

35 papers7 papers in the last four weeks, up 133% on the four weeks before. 0.0% of all new papers.

  1. Not All Retained Clients Are Equal: Client Selection for Federated Unlearning

    Oct 22, 2026Thanh Linh Nguyen, Quoc-Viet PhamFederated UnlearningClient Selection in Federated Learning

    School of Computer Science and Statistics, Trinity College Dublin, Ireland

  2. LiBRA: Logit Fingerprints for Byzantine-Robust Client Selection in Federated Learning

    Oct 19, 2026Saheed A. Tijani, Frank Jiang, Ming LiuByzantine-Robust Federated LearningFederated Learning Aggregation

    Centre for Cyber Security Research and Innovation (CSRI), Deakin University, Geelong, Australia.

  3. Federated Learning for Malware Image Classification under Data Heterogeneity

    Oct 18, 2026Victor Taiwo, Cemal Nisan, Muhammad Athallah +2Non-IID Federated LearningFederated Learning

    Department of Computer Engineering, Koç University, Istanbul, Türkiye · Informatics Institute, Istanbul Technical University, Istanbul, Türkiye

  4. SPADE-KD: Peer-to-Peer Collaborative Learning under Heterogeneous Settings

    Oct 15, 2026Harikrishna Kuttivelil, Katia ObraczkaFederated Knowledge DistillationAdaptive Knowledge Distillation

    University of California, Santa Cruz

  5. PFedMA: Who Should Join Next as Federated Multi-Agent Orchestration Evolves?

    Oct 14, 2026Shuai Li, Shengyuan Liu, Fan Qi +1Multi-Agent LLM SystemsMulti-Agent Collaboration

    Tianjin University of Technology · Inner Mongolia University · Institute of automation, Chinese academy of science

  6. Client and Training Data Selection for Computationally Efficient Synchronized Federated Learning

    Sep 30, 2026Muzaffer Citir, Hiroki Nishikawa, Sangyoung ParkClient Selection in Federated LearningNon-IID Federated Learning

    Smart Mobility Systems, Technical University of Berlin, Germany · Graduate School of Information Science and Technology, The University of Osaka, Japan

  7. Agentic Federated Learning: Rule-Based Client and Server Agents for Adaptive Training

    Sep 28, 2026Deepthy K. Bhaskar, VP Binu, B MinimolFederated Learning AggregationClient Selection in Federated Learning

    Department of Computer Engineering, Model Engineering College, APJ Abdul Kalam Technological University, Thiruvananthapuram 695016, Kerala, India. · Department of Biomedical Engineering, Model Engineering College, APJ Abdul Kalam Technological University, Thiruvananthapuram 695016, Kerala, India.

  8. Adaptive Determinantal Client Scheduling in Federated Learning

    Sep 20, 2026Wen Xu, Ben Liang, Gary Boudreau +1Client Selection in Federated LearningFederated Learning

    University of Toronto, Canada · Ericsson, Canada

  9. Adaptive Bayesian Partner Selection for Federated Clinical Centers

    Sep 15, 2026Navid Seidi, Satyaki Roy, Sajal K. DasHealthcareCommunication-Efficient Distributed Training

    Department of Computer Science Missouri University of Science and Technology Rolla, MO, USA · Department of Mathematical Sciences University of Alabama in Huntsville Huntsville, AL, USA

  10. Sylvas: Synergistic Learning Value based Device Scheduling in Federated Continual Learning

    Sep 14, 2026Yuxuan Sun, Yuxuan Bai, Tan Chen +2Client Selection in Federated LearningFederated Continual Learning

    School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China · Department of Electronic Engineering, Tsinghua University, Beijing 100084, China, and the Beijing National Research Center for Information Science and Technology

  11. Active Client Selection in Federated Trajectory Prediction with Uncertainty-Awareness and Heterogeneous Complexity

    Aug 27, 2026Yiming Xie, Muzi Peng, Fei Miao +2Client Selection in Federated LearningTrajectory Prediction

    Northeastern University, Boston, MA, USA · University of Connecticut, Storrs, CT, USA

  12. Beyond Non-IID: Learner--Client Distribution Mismatch in Federated Learning

    Aug 27, 2026Yiming Xie, Lili Su, Ningfang MiClient Selection in Federated LearningNon-IID Federated Learning

    Department of Electrical and Computer Engineering Northeastern University, Boston, MA, USA

  13. Designing Sustainable Federated Learning as a Service using Neural Architecture Search

    Aug 14, 2026Keya Patel, Sajib Mistry, Sheik Fattah +4Federated LearningNeural Architecture Search

    School of Electrical Engineering, Computing and Mathematical Sciences Curtin University, Perth, Australia · King Fahd University of Petroleum and Minerals, Saudi Arabia · Umeå University, Umeå, SE-90187, Sweden

  14. Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning

    Aug 3, 2026Yuan-Heng Tsai, Li-Hsing Yen, Yan-Wei ChenAlgorithmic FairnessClient Selection in Federated Learning

    Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.

  15. Robust Federated Learning Under Real-World Client Churn

    Jul 8, 2026Dhruv Garg, Neha Lakhani, Debopam Sanyal +3Federated Learning AggregationCommunication-Efficient Distributed Training

    Georgia Tech USA · Cisco Research USA

  16. SCOPE-FL: A Strategy-proof Chain-based Optimal pareto efficient Federated Learning System

    Jun 16, 2026Seyed Salar Ghazi, Kaiwen Zhang, Mehdi feizi +1Hierarchical Federated LearningMechanism Design

    Department of Software and IT Engineering, École de Technologie Supérieure (ÉTS), Montréal, QC H3C 1K3, Canada · Department of Economics, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran · Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada

  17. EvoCSFL: Surrogate-Assisted Evolutionary Client Selection for Efficient and Robust Federated Learning

    Jun 5, 2026Lin Qiang, Sun Xiaoyan, Hu Yao +1Client Selection in Federated LearningFederated Learning

    1Jiangnan University · 2The Hong Kong Polytechnic University

  18. Personalized Federated Learning by Energy-Efficient UAV Communications

    May 24, 2026Shiqian Guo, Jianqing Liu, Beatriz LorenzoEnergy-Efficient MLClient Selection in Federated Learning

    Department of Computer Science, North Carolina State University, Raleigh, NC 27606 USA · University of Massachusetts, Amherst, MA 01003 USA

  19. Hardware-Aware Federated Learning for Speech Emotion Recognition

    May 23, 2026Beyazit Bestami Yuksel, Emrah DikbiyikCommunication-Efficient Distributed TrainingClient Selection in Federated Learning

    Computer Engineering Istanbul Technical University Istanbul, Turkey · Department of Computer Technologies Istanbul University-Cerrahpaşa Istanbul, Turkey

  20. Federated Learning over Human-Body Communication for On-Body Edge Intelligence: A Survey, Taxonomy, and BODYFED-HBC Scheduling Vignette

    May 22, 2026Koffka KhanClient Selection in Federated LearningFederated Learning

    Department of Computing and Information Technology The University of the West Indies, St. Augustine, Trinidad and Tobago

  21. Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning

    May 20, 2026Adda Akram Bendoukha, Heber Hwang Arcolezi, Nesrine Kaaniche +1Client Selection in Federated LearningNon-IID Federated Learning

    Samovar, T´el´ecom SudParis, Institut Polytechnique de Paris, France · ´ETS Montr´eal · CEA-LIST

  22. Byzantine-Resilient Federated Learning via QUBO-Based Client Selection on Quantum Annealers

    May 14, 2026Andras Ferenczi, Sutapa Samanta, Dagen Wang +1Federated Learning AggregationByzantine-Robust Federated Learning

    American Express Co · American Express Co. · Columbia University

  23. Federated Client Selection under Partial Visibility: A POMDP Approach with Spatio-Temporal Attention

    May 12, 2026Qijun Hou, Yuchen Shi, Pingyi Fan +1Client Selection in Federated LearningSpatio-Temporal Attention

    Department of Electronic Engineering, BNRist, Tsinghua University, Beijing, China · Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong

  24. VARS-FL: Validation-Aligned Client Selection for Non-IID Federated Learning in IoT Systems

    May 7, 2026Mohamed Lakas, Mohamed Amine FerragIoT Intrusion DetectionClient Selection in Federated Learning

    College of Information Technology, United Arab Emirates University, UAE

  25. Who Trains Matters: Federated Learning under Enrollment and Participation Selection Biases

    Apr 29, 2026Gota MorishitaInverse Probability WeightingFederated Learning Aggregation

    Centre for Brain, Mind and Markets University of Melbourne Parkville, Victoria, Australia

  26. Agentic Federated Learning: The Future of Distributed Training Orchestration

    Apr 6, 2026Rafael O. Jarczewski, Gabriel U. Talasso, Leandro Villas +1Federated LearningClient Selection in Federated Learning

    Institute of Computing, University of Campinas and Hub of Artificial Intelligence and Cognitive Architectures - H.IAAC, Campinas, Brazil

  27. FedTrident: Resilient Road Condition Classification Against Poisoning Attacks in Federated Learning

    Mar 19, 2026Sheng Liu, Panos PapadimitratosFederated LearningClient Selection in Federated Learning

    Networked Systems Security Group, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden

  28. FedACT: Concurrent Federated Intelligence across Heterogeneous Data Sources

    Mar 11, 2026Md Sirajul Islam, Isabelle G Chapman, N I Md Ashafuddula +4Online Federated LearningFederated Learning

    School of Computing and Informatics, University of Louisiana at Lafayette, USA · Department of Computer and Information Sciences, University of Delaware, USA · Department of Computer Science, University of Illinois at Urbana-Champaign, USA

  29. FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data

    Mar 9, 2026Daniel M. Jimenez-Gutierrez, Giovanni Giunta, Mehrdad Hassanzadeh +3Client Selection in Federated LearningNon-IID Federated Learning

    Sapienza University of Rome Via Ariosto 25 Rome, 00185, Italy

  30. Noise-aware Client Selection for carbon-efficient Federated Learning via Gradient Norm Thresholding

    Mar 4, 2026Patrick Wilhelm, Inese Yilmaz, Odej KaoClient Selection in Federated LearningFederated Learning

    BIFOLD Technische Universität Berlin, Germany · Technische Universität Berlin, Germany