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. Communication-Efficient Clustered Federated Learning in Non-IID Wireless Networks

    Oct 28, 2026Martin Isaksson, Rickard Cöster, Šarūnas Girdzijauskas +1Federated LearningClustered Federated Learning

    Ericsson AB and KTH Royal Institute of Technology, Sweden · Ericsson AB, Sweden · KTH Royal Institute of Technology and RISE Research Institutes of Sweden, Sweden

  2. From Classifier Heads to Encoder Prototypes: Personalized Federated Learning with Head-Derived Clustering

    Oct 21, 2026Amirardalan Dehghanpour, Arian Noori, Ehsan Tanghatari +2Federated LearningNon-IID Federated Learning

    Sharif University of Technology · Tehran Institute for Advanced Studies (TeIAS)

  3. The Dynamics of Collapse and Recovery in Clustered Federated Learning

    Oct 20, 2026Priyanka Sinha, Snigdhansu ChatterjeeClustered Federated Learning

    University of Maryland, Baltimore County

  4. RIPPLE in Still Water: Zero-Shot Clustering in Federated Learning with Wavelet Scattering Transform

    Oct 2, 2026Alessandro LicciardiWavelet Scattering TransformsClustered Federated Learning

    Department of Mathematical Sciences Politecnico di Torino, Turin, Italy

  5. CF-LoRA: Decoupled Factor Aggregation and Adaptation-Aware Client Clustering for Federated LoRA Fine-Tuning

    Sep 29, 2026Mengjun Yi, Langxing Yang, Suhan Guo +2Federated Learning AggregationFederated Fine-Tuning

    State Key Laboratory for Novel Software Technology · School of Artificial Intelligence School of Electronic Science and Engineering Nanjing University

  6. Federated Clustering with Unknown Local and Global Cluster Cardinalities

    Sep 29, 2026Mitushi Goyal, Tarun S., Riddhanya Senapathi +1Cluster Number EstimationClustering

    BITS Pilani K. K. Birla Goa Campus, Goa, India.

  7. Momentum-Guided Federated Split Distillation for Personalized Temporal Edge Intelligence

    Sep 25, 2026Ahmed-Rafik Baahmed, Jean-François Dollinger, Amine Brahmia +1Federated Knowledge DistillationFederated Learning

    LINEACT · CESI, CESI LINEACT UR 7527, Strasbourg, France

  8. Concurrent Split Learning Through Stable Client Clustering

    Sep 24, 2026Mohammad Kohankhaki, Valentin Rentschler, Anke SchmeinkSplit Federated LearningClustered Federated Learning

    Chair of Information Theory and Data Analytics, RWTH Aachen University, Aachen, Germany

  9. Joint Domain-Class Modeling for Federated Learning Under Feature Skew

    Sep 19, 2026Sina Najafi, Mostafa Tavassolipour, Seyed Pooya ShariatpanahiFederated LearningHeterogeneous Federated Learning

    School of Electrical and Computer Engineering College of Engineering, University of Tehran Tehran, Iran

  10. Opinion Dynamics-based Coalition Formation for Federated Learning in Heterogeneous IoT Systems

    Sep 17, 2026Mohammed El Hanjri, Anas Abouaomar, Hamidou Tembine +1Opinion DynamicsFederated Learning Aggregation

    ENSIAS, Mohammed V University in Rabat, Morocco. · Department of Electrical and Computer Engineering, School of Engineering, University of Quebec at Trois-Rivieres, Quebec, Canada.

  11. Task-Aware Federated Fine-Tuning for MoE-based Large Language Models

    Sep 11, 2026Tingqi Wang, Hongyu Ke, Haoxin Wang +2Federated Fine-TuningNon-IID Federated Learning

    Department of Computer Science, Georgia State University Atlanta, GA, United States

  12. Private Computation Space: Experience with Trusted Multi-Cluster Federated Learning for Agriculture

    Sep 1, 2026Shuangyu Lei, Muhammad Salman Abid, Jacob Belding +17Privacy-Preserving Federated LearningFederated Learning

    Cornell University USA

  13. MoE Router-Guided Clustering for Heterogeneous Federated Instruction Tuning

    Aug 15, 2026Ankita Sharma, Bahar Farahani, Sanaz Rahimi Moosavi +3Federated LearningHeterogeneous Federated Learning

    Arizona State University · Member, IEEE · California State University, Dominguez Hills +2

  14. Hierarchical Federated Transfer Learning in Digital Twin-Based Vehicular Networks

    Aug 12, 2026Qasim Zia, Saide Zhu, Haoxin Wang +2Hierarchical Federated LearningDigital Twins

    Georgia State University, Atlanta, GA, 30303 · Penn State Berks, Reading, PA, 19610

  15. Hierarchical Multi-Task Federated Learning in VANETs

    Aug 8, 2026M. Saeid HaghighiFard, Sinem ColeriHierarchical Federated LearningCommunication-Efficient Distributed Training

    Department of Electrical and Electronics Engineering, Koc University, Istanbul, Turkey

  16. Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalization

    Aug 2, 2026Furkan Bagci, Busra Tegin, Mohammad Kazemi +1Federated Learning AggregationNon-IID Federated Learning

    Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, IL 60607 USA · IETR-UMR CNRS 6164, Centrale-Supélec Rennes Campus, Cesson-Sévigné 35576, France · Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2BT, U.K. +1

  17. FeDepth: Federated Learning for Depth Estimation under Robot Heterogeneity

    Aug 2, 2026Ganghyeon Lee, Inha Lee, Junhee Lee +3Depth EstimationNon-IID Federated Learning

    Ulsan National Institute of Science and Technology (UNIST), Republic of Korea

  18. Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata

    Jul 30, 2026Michael Ben Ali, Imen Megdiche, André Péninou +1Privacy-Preserving MLClustered Federated Learning

    UT3, IRIT, CNRS · INU Champollion, ISIS, IRIT, CNRS · UT2J, IRIT, CNRS +1

  19. Stable and Budget-Feasible Coalition Formation for Clustered Federated Learning: A Hedonic Potential-Game Approach

    Jul 29, 2026Cengis HasanCooperative Game TheoryCoalition Formation

    Cognifinity, Luxembourg

  20. An Informativeness-based Clustered Federated Learning Method for Reliable Traffic Prediction in Managed Wi-Fi Networks

    Jul 29, 2026Luca Barbieri, Gianluca Fontanesi, Lorenzo Galati Giordano +2Wireless Local Area NetworksClustering

    Radio Systems Research, Nokia Bell Labs, Stuttgart, Germany · Nokia Spain, Madrid

  21. Discovering Collaboration from Novelty: Random Network Distillation for Clustered Federated Learning

    Jun 29, 2026Davide Domini, Gianluca Aguzzi, Ivana Dusparic +2Unsupervised ClusteringNon-IID Federated Learning

    University of Bologna Cesena, Italy · Trinity College Dublin Dublin, Ireland

  22. C2FL: Clustered Continual Federated Learning under Spatial and Temporal Drift

    Jun 16, 2026Davide Domini, Gianluca Aguzzi, Lorenzo Pellegrini +2Federated Learning AggregationFederated Continual Learning

    University of Bologna · Aarhus University

  23. Range Penalization: Theoretical Insights with Applications in Federated Learning

    Jun 9, 2026Yiyuan She, Zhaojun Hu, Yifan SunStatistical Learning TheoryClustered Federated Learning

  24. Towards Serverless Semi-Decentralized Federated Learning with Heterogeneous Optimizers

    Jun 4, 2026Su Wang, Mung Chiang, H. Vincent PoorCommunication-Efficient Distributed TrainingClustered Federated Learning

    Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA · Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA

  25. TITAN-FedAnil+: Trust-Based Adaptive Blockchain Federated Learning for Resource-Constrained Intelligent Enterprises

    Jun 3, 2026Muhammad Hadi, Muhammad Jahangir, Talha Shafique +1Byzantine-Robust Federated LearningNon-IID Federated Learning

    School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad, Pakistan

  26. Hierarchical Federated Learning with Dynamic Clustering and Adaptive Regularization for Robust Infrastructure Inspection

    Jun 2, 2026Yuhu Feng, Keisuke Maeda, Takahiro Ogawa +1Hierarchical Federated LearningNon-IID Federated Learning

  27. FedMTFI: Feature Importance Based Optimized Multi Teacher Knowledge Distillation in Heterogeneous Federated Learning Environment

    Jun 1, 2026Nazmus Shakib Shadin, Aaron Cummings, Xinyue Zhang +1Non-IID Federated LearningMulti-Teacher Knowledge Distillation

    Department of Computer Science, Kennesaw State University, Marietta, GA, 30060 USA

  28. Automated Byzantine-Resilient Clustered Decentralized Federated Learning for Battery Intelligence in Connected EVs

    May 20, 2026Mouhamed Amine Bouchiha, Abdelaziz Amara Korba, Yacine Ghamri-DoudaneByzantine-Robust Federated LearningDecentralized Learning

    SAMOVAR, T´el´ecom SudParis, Paris, France · Department of Computer Science, German University of Technology in Oman (GUtech), Muscat, Oman · L3i, La Rochelle University, La Rochelle, France

  29. Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learning

    May 12, 2026Satwat Bashir, Tasos Dagiuklas, Muddesar IqbalHierarchical Federated LearningFederated Learning Aggregation

    Computer Science and Informatics · London South Bank University · London, UK +1

  30. COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication

    May 11, 2026Ben Rachmut, Luise Ge, Ning Zhang +2Clustered Federated LearningFederated Learning

    Washington University in St. Louis