Clustered Federated Learning

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Overview

Clustered federated learning studies how to group data-holding clients so each group can learn a model suited to its members, addressing the uneven data distributions that can make a single global model perform poorly. Methods infer groups from update or gradient similarity, metadata, learned representations, or model behavior, then use cluster-specific aggregation, personalization, or knowledge distillation; some use soft or changing assignments rather than fixed groups. The approach is applied in settings such as mobile devices, healthcare, vehicles, and language-model fine-tuning, often with goals of improving accuracy while limiting communication. A central challenge is finding useful clusters when their number and membership are unknown or shifting, while balancing privacy, communication cost, and the risk that specialization reduces knowledge sharing.

Latest papers

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46 papers5 papers in the last four weeks, down 17% on the four weeks before. 0.0% of all new papers.

  1. CLAD: A Clustered Label-Agnostic Federated Learning Framework for Joint Anomaly Detection and Attack Classification

    May 7, 2026Iason Ofeidis, Nikos Papadis, Randeep Bhatia +2IoT Intrusion DetectionNetwork Intrusion Detection

    Yale University, New Haven, CT, USA · Nokia Bell Labs, Murray Hill, NJ, USA

  2. FMCL: Class-Aware Client Clustering with Foundation Model Representations for Heterogeneous Federated Learning

    Apr 30, 2026Mahad Ali, Laura J. BrattainNon-IID Federated LearningClustered Federated Learning

    University of Central Florida, Orlando FL 32816, USA

  3. Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation

    Apr 22, 2026Jie Xu, Haaris Mehmood, Rogier Van Dalen +2Federated Learning AggregationNon-IID Federated Learning

    Samsung R&D Institute UK (SRUK) · Samsung AI Centre Cambridge

  4. FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning

    Apr 21, 2026Abdulmoneam Ali, Ahmed ArafaClusteringNoisy-Label Learning

    Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, NC 28223, USA

  5. Task-Centric Personalized Federated Fine-Tuning of Language Models

    Mar 30, 2026Gabriel U. Talasso, Meghdad Kurmanji, Allan M. de Souza +2Federated LearningFederated Fine-Tuning

    Universidade Estadual de Campinas · University of Cambridge · Flower Labs

  6. DriftGuard: Mitigating Asynchronous Data Drift in Federated Learning

    Mar 19, 2026Yizhou Han, Di Wu, Blesson VargheseFederated LearningFederated Continual Learning

    School of Computer Science, University of St Andrews, UK

  7. 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

  8. FedPrism: Adaptive Personalized Federated Learning under Non-IID Data

    Mar 9, 2026Prakash Kumbhakar, Shrey Srivastava, Haroon R LoneFederated LearningNon-IID Federated Learning

    IISER Bhopal, Bhopal, India

  9. A Behaviour-Aware Federated Forecasting Framework for Distributed Stand-Alone Wind Turbines

    Mar 5, 2026Bowen Li, Xiufeng Liu, Maria Sinziiana AstefanoaeiWind Power ForecastingClustered Federated Learning

    IT University of Copenhagen, Denmark. · Technical University of Denmark (DTU), Denmark

  10. PTOPOFL: Topology-Guided Personalised Federated Learning with Persistent-Homology Client Descriptors

    Mar 4, 2026Kelly L Vomo-Donfack, Adryel Hoszu, Grégory Ginot +1Privacy-Preserving Federated LearningFederated Learning

    Université Sorbonne Paris Nord, LAGA, CNRS, UMR 7539, Laboratoire d’excellence Infibrex, F-93430 Villetaneuse, France · Instituto de Hortofruticultura Subtropical y Mediterránea La Mayora (IHSM), Universidad de Málaga–Consejo Superior de Investigaciones Científicas, Málaga, Spain

  11. HiLoRA: Hierarchical Low-Rank Adaptation for Personalized Federated Learning

    Mar 3, 2026Zihao Peng, Nan Zou, Jiandian Zeng +4Federated LearningFederated Fine-Tuning

    Beijing Normal University · Zhengzhou University

  12. CA-AFP: Cluster-Aware Adaptive Federated Pruning

    Mar 2, 2026Om Govind Jha, Harsh Shukla, Haroon R. LoneFederated LearningNeural Network Pruning

    IISER Bhopal Bhopal, India

  13. FedDAG: Clustered Federated Learning via Global Data and Gradient Integration for Heterogeneous Environments

    Feb 26, 2026Anik Pramanik, Murat Kantarcioglu, Vincent Oria +1Clustered Federated LearningFederated Learning

    New Jersey Institute of Technology, USA · Virginia Tech, USA

  14. Wireless Federated Multi-Task LLM Fine-Tuning via Sparse-and-Orthogonal LoRA

    Feb 24, 2026Nuocheng Yang, Sihua Wang, Ouwen Huan +3Federated Fine-TuningFederated Learning

    Beijing Laboratory of Advanced Information Network, and the Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China · Department of Electrical and Computer Engineering and Institute for Data Science and Computing, University of Miami, Coral Gables, FL, 33146 USA · Information Systems Technology and Design Pillar, Singapore University of Technology and Design, 487372, Singapore

  15. Reinforcement Federated Learning Method Based on Adaptive OPTICS Clustering

    Jun 22, 2023Tianyu Zhao, Junping Du, Yingxia Shao +1Federated Learning AggregationClustering

    Beijing Key Laboratory of Intelligent Communication Software and Multimedia, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China

  16. StoCFL: A Stochastically Clustered Federated Learning Framework for Non-IID Data with Dynamic Client Participation

    Mar 2, 2023Dun Zeng, Xiangjing Hu, Shiyu Liu +3Non-IID Federated LearningClustered Federated Learning

    University of Electronic Science and Technology of China · Peng Cheng Lab · Harbin Institute of Technology, Shenzhen +1