Federated Fine-Tuning

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Overview

Federated fine-tuning studies how multiple data holders can adapt a shared pretrained model while keeping examples local, enabling collaboration in healthcare, finance, language, vision, and edge settings. Common approaches include parameter-efficient methods such as LoRA, QLoRA, and prompt tuning; client-server aggregation or split learning; and distillation when model updates are costly or architectures differ. Since clients have different data, tasks, and hardware, methods use personalized adapters, clustering or task-aware routing, update alignment, and compression, often evaluated on non-IID language, vision, and multimodal benchmarks. Keeping raw data local does not prevent gradient leakage, membership inference, poisoning, or unequal outcomes for weaker clients; differential privacy and stronger security can reduce utility and add cost, making realistic measurements of communication, memory, energy, and latency important.

Latest papers

CardsList

115 papers17 papers in the last four weeks, up 31% on the four weeks before. 0.1% of all new papers.

  1. Communication-Efficient Federated Domain Generalization in Edge Networks via Dynamic Prototype Regulation

    Oct 19, 2026Yuankun Xia, Hui Wang, Ling Yuan +3Federated LearningFederated Domain Generalization

    School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China

  2. PFedQFT: Personalized Federated Quantum-Inspired Fine-Tuning for Medical Large Language Models

    Oct 19, 2026Qihan Huang, Lingxia Zhang, Guye Zhang +2Federated Fine-TuningFederated Learning

    Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, China · Key Laboratory of Quantum Physics and Photonic Quantum Information, Ministry of Education, University of Electronic Science and Technology of China, Chengdu, Sichuan, China · Sichuan Provincial Key Laboratory for Human Disease Gene Study and the Center for Medical Genetics, Department of Laboratory Medicine, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China +1

  3. Federated Art Chase: Privacy Leakage and Data Poisoning in Federated Fine-Tuning

    Oct 15, 2026Vasileios Perifanis, Nikolaos PavlidisFederated LearningFederated Fine-Tuning

    Indigma Innovations Greece

  4. Optimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets

    Oct 14, 2026Gwen Legate, Rahaf Aljundi, Eugene BelilovskyFederated LearningHeterogeneous Federated Learning

    Concordia University · Mila · Toyota Motor Europe

  5. FedGraph: Defending Federated Large Language Model Fine-Tuning Against Backdoor Attacks via Graph-Based Aggregation

    Oct 13, 2026Xi Chen, Chunyi Zhou, Rui Zeng +3Backdoor Attacks in Federated LearningLLM Backdoor Attacks

    Zhejiang University, Hangzhou, China

  6. BLEND: Balancing Personalization vs. Generalization in Federated Vision-Language Models

    Oct 9, 2026Payam Abdisarabshali, Fardis Nadimi, Kasra Borazjani +3VLM AdaptationFederated Fine-Tuning

    University at Buffalo–SUNY · Adobe · Zhejiang University

  7. Personalized Federated Direct Preference Optimization via LoRA Adapters

    Oct 9, 2026Zhanhong Jiang, Soumik SarkarPersonalized Language Model AlignmentDirect Preference Optimization

    Translational AI Center, Iowa State University, Ames, IA 50011 USA · Department of Mechanical Engineering, Iowa State University, Ames, IA 50011 USA

  8. HE-OFT: Privacy-Preserving One-Shot Federated Fine-Tuning under Homomorphic Encryption

    Oct 6, 2026Halil İbrahim Kanpak, Sinem Sav, Alptekin KüpçüPrivacy-Preserving MLFederated Fine-Tuning

    Department of Computer Engineering, Kocı University, İstanbul, Türkiye · Department of Computer Engineering, Bilkent University, Ankara, Türkiye

  9. Collaborative Personalized Preference Alignment for LLMs under Data Deficiency

    Oct 5, 2026Liyan Yang, Yige Yuan, Zhiqin YangPersonalized Language ModelsPersonalized Language Model Alignment

    City University of Hong Kong · University of Washington · The Hong Kong University of Science and Technology

  10. Beyond Domain-Level Adaptation: Margin-Oriented Semantic-Appearance Interaction Correction for Personalized Federated Vision-Language Models

    Oct 1, 2026Wentao Yue, Qingyu Mao, Tianyou Lai +2VLM AdaptationFederated Fine-Tuning

  11. FedFit: Federated Fine-Tuning of LLMs via Vector-Bank Parameterization and Quantization

    Oct 1, 2026Hang Zou, Chao Zhang, Yuzhi Yang +3Federated Learning AggregationLow-Rank Adaptation

    6G Research Center, Khalifa University, 127788 Abu Dhabi, UAE · Department of Computer Science, Central South University, 410083, Changsha, China · Université de Lorraine, CNRS, CRAN, F-54000 Nancy, France

  12. Federated Learning for LLMs over Mobile Networks: Issues and Solutions in the RAN Transport

    Oct 1, 2026Emilio Paolini, Andrea Pinto, Flavio Esposito +1Federated Learning AggregationAsynchronous Federated Learning

    Tecip Institute, Scuola Superiore Sant’Anna, Pisa, Italy · Computer Science Department, Saint Louis University, St. Louis, USA

  13. From Task Mixtures to Specialized Experts

    Sep 30, 2026Hojat Allah Salehi, Mehrdad Mahdavi, Andrew Arash Mahyari +1Multi-Task LearningMixture-of-Experts Models

    Florida International University, Miami, FL, USA · Security, Optimization, and Learning for InterDependent networks laboratory (solid lab), Miami, FL, USA · The Pennsylvania State University, University Park, PA, USA +1

  14. Unapologetically Distributed: A Call for Decentralized Document Analysis

    Sep 30, 2026Adrià Molina, Oriol Ramos Terrades, Josep LladósOOD GeneralizationDecentralized Learning

    Centre de Visió per Computador Universitat Autònoma de Barcelona Bellaterra, Catalonia · Computer Science Department Universitat Autònoma de Barcelona, Bellaterra, Catalonia

  15. FedLAFP: Low-Rank Aggregation Meets Full-Rank Personalization in Federated Fine-Tuning

    Sep 29, 2026Mengjun Yi, Huaian Gu, Yinghao Ai +2Low-Rank AdaptationFederated Fine-Tuning

    State Key Laboratory for Novel Software Technology · School of Artificial Intelligence · School of Computer Science +1

  16. 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

  17. RoboFL: Federated Expert Assembly for World Action Models

    Sep 28, 2026Rongyu Zhang, Ruizhi Fan, Yunfan Lou +8Mixture of ExpertsWorld Action Models

    Nanjing University · Hong Kong University of Science and Technology · State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University

  18. CFLoRA: Federated Fine-tuning of LLMs with Complementary Factors for Error-free Aggregation

    Sep 27, 2026Yanan Ma, Qiyuan Chen, Zihan Fang +2Federated Fine-TuningFederated Learning

    City University of Hong Kong · The University of Hong Kong

  19. FAVoR: Measuring and Mitigating Author-Style Homogenization in Federated Personalized Generation

    Sep 25, 2026Lu Han, Jingyao Zhang, Katy Ilonka Gero +1Personalized Language ModelsPersonalized Text Generation

    School of Computer Science, The University of Sydney Sydney, NSW 2006, Australia

  20. Coupling Perception and Reasoning in Federated Multimodal Graph Foundation Models

    Sep 24, 2026Zekai Chen, Xun Wu, Hailin Zhang +7Multimodal Federated LearningGraph Foundation Models

    Beijing Institute of Technology · Shandong University · Institute of Information Engineering +1

  21. Perplexity Predicts Protection: Choosing Pretrained Backbones for Worst-Client Fairness in Federated Parameter-Efficient Fine-Tuning

    Sep 20, 2026Kiran Naseer, Samreen Azhar, Umar Shoaib +2Federated Fine-TuningFederated Learning

    Department of Computer Science, University of Gujrat, Gujrat, Pakistan · Department of Cybersecurity, College of Engineering, Al Ain University, Al Ain, UAE · Department of Computer Science, College of Computer, Qassim University, Qassim 52571, Saudi Arabia

  22. Co-VLA: Consensus-based Federated Training for Vision-Language-Action Models

    Sep 17, 2026Haolong Li, Guner Dilsad Er, Michael Muehlebach +1Vision-Language-Action ModelsFederated Fine-Tuning

    Intelligent Perception in Technical Systems, University of Augsburg, Germany · Max Planck Institute for Intelligent Systems, Germany

  23. CIDERS: Cloud-Edge LLM Collaborative Learning via Accelerating Personalized Bilevel Optimization

    Sep 14, 2026Victor H. Chen, Hairui Yu, Stella K. Chung +1Bilevel OptimizationFederated Fine-Tuning

  24. FLoKD: Adaptive Knowledge Distillation for Federated Low-Rank LLM over Wireless Networks

    Sep 11, 2026Xinlu Zhang, Na Yan, Yang Su +2LLM Fine-TuningFederated Knowledge Distillation

    Department of Engineering, King’s College London, Strand, London WC2R 2LS, U.K.

  25. A Measured Communication-Quality Frontier for Federated LoRA Fine-Tuning with Adaptive Phase-Switching

    Sep 11, 2026Jerry Adams FranklinFederated Fine-TuningLLM Fine-Tuning

    Independent Researcher, Fairport, NY, 14450, USA.

  26. 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

  27. Privacy-Preserving Split Learning for Federated LLM Fine-Tuning

    Sep 9, 2026Heng Jin, Chaoyu Zhang, Hexuan Yu +2Privacy-Preserving Language ModelsSplit Federated Learning

    Virginia Tech, Arlington, VA, USA

  28. FedSubMuon: Communication-Efficient Federated LLM Fine-Tuning via Structured Subspace Muon

    Sep 5, 2026Shaolong Chen, Youming Tao, Shuzhen Chen +3Subspace OptimizationFederated Fine-Tuning

    King Abdullah University of Science and Technology (KAUST) · Provable Responsible AI and Data Analytics (PRADA) Lab · Imperial College London +3

  29. Federated LoRA Adaptation of BiomedCLIP Across Four International Chest X-Ray Cohorts

    Sep 2, 2026Sanjaya Poudel, Nirajan Kunwor, Manish Dhakal +2Federated Fine-TuningFederated Learning

    North Carolina A&T State University · Tribhuvan University · University of Tennessee-Knoxville +1

  30. Breaking the Structural Identity: Personalized Federated LoRA Fine-tuning under Rank Heterogeneity

    Sep 1, 2026Lei Wang, Jieming Bian, Letian Zhang +1Non-IID Federated LearningLow-Rank Adaptation

    University of Florida · Gainesville, FL 32611 · Middle Tennessee State University +1