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. FISGuard: Defending Against Membership Inference via Fixed Input Subspaces

    Aug 28, 2026Haocheng Jiang, Hua ShenPrivacy-Preserving Language ModelsMembership Inference Attacks

    Hubei University of Technology

  2. SplitLite: Low-Rank Residual Compression for Split Learning

    Aug 24, 2026Tao Li, Yulin Tang, Qi Guo +1Split Federated LearningCommunication-Efficient Distributed Training

    Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong SAR, China · Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA

  3. Unveiling the Depth-Performance Dilemma in Split-Federated Fine-tuning of LLMs

    Aug 23, 2026Hariharan Ramesh, Someshwaran Murugaiyan, Jyotikrishna DassSplit Federated LearningFederated Fine-Tuning

    School of Electrical, Computing & Software Engineering, University of Arizona, Tucson, AZ, USA · Vellore Institute of Technology, Vellore, India

  4. Thermo-FL: Thermal-Aware Robust Federated Fine-Tuning of Large Language Models for Edge AI

    Aug 21, 2026Shiva Shrestha, Kazi Shaharair Sharif, Zongxing Xie +3Federated Fine-TuningFederated Learning

    Department of Computer Science, Kennesaw State University Marietta, GA 30060, USA · School of Data Science and Analytics, Kennesaw State University Marietta, GA 30060, USA · Department of Information Technology, Kennesaw State University Marietta, GA 30060, USA

  5. When Adaptation Hurts: Split Sensitivity and Person-Level Negative Transfer in Federated Wearable Onboarding

    Aug 18, 2026Rahil Aftab, Vineet Kumar Rakesh, Soumya Mazumdar +1Federated LearningFederated Fine-Tuning

    Department of Computer Science and Engineering Jamia Hamdard New Delhi 110062, India · Engineering Science Homi Bhabha National Institute Anushaktinagar, Mumbai 400094, Maharashtra, India Computer and Informatics Group Variable Energy Cyclotron Centre 1/AF, Bidhannagar, Kolkata 700064, West Bengal, India · Department of Computer Science and Business Systems Gargi Memorial Institute of Technology Affiliated to Maulana Abul Kalam Azad University of Technology Balarampur, Mouza Beralia, Baruipur, Kolkata 700144, West Bengal, India

  6. FedPref: Federated Preference Learning for Structured Radiology Report Extraction

    Aug 17, 2026Flint Xiaofeng Fan, Cheston Tan, Yew-Soon Ong +1Clinical Information ExtractionFederated Learning

    ETH Zurich, Zurich, Switzerland · Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

  7. When Is Shallow Enough? Adaptive Split Federated Learning with Client-Specific Sufficiency Estimation

    Aug 16, 2026Wenhao Yuan, Chenchen Lin, Wentao Hu +4Split Federated LearningHeterogeneous Federated Learning

    The Hong Kong Polytechnic University Hong Kong, SAR, China

  8. FedPA-LoRA: Product-Aligned Framework for Mitigating Aggregation and Initialization Errors in Heterogeneous Federated LoRA

    Aug 15, 2026Juseok Jeon, Ramy E. Ali, Doyun Kwon +3Federated Fine-TuningFederated Learning

    DGIST Daegu, Republic of Korea · Samsung San Diego, USA

  9. Global Federated Learning Strategies for Building Efficient Personalized Models

    Aug 15, 2026Seongyoon KimFederated LearningHeterogeneous Federated Learning

    한국과학기술원 Korea Advanced Institute of Science and Technology 산업및시스템공학과

  10. Federated Prompt Learning: A Unified Framework, Empirical Analysis, and Future Directions

    Aug 14, 2026Qinglin Yang, Chen Qiu, Hongyuan Zhang +3Federated LearningPrivacy-Preserving Language Models

    Cyberspace Institute of Advanced Technology, Guangzhou University; Guangdong Key Laboratory of Industrial Control System Security; Huangpu Research School of Guangzhou University, Guangzhou, Guangdong, China · Iwate Biotechnology Research Center, Iwate, Japan · Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China

  11. SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks

    Aug 10, 2026Yue Xia, Tayyebeh Jahani-Nezhad, Mayank Bakshi +1Federated Learning AggregationLow-Rank Adaptation

    Technical University of Munich Munich, 80333 Germany · Technische Universität Berlin Berlin, 10587 Germany · Northern Arizona University Flagstaff, AZ 86011 USA

  12. Rethinking Factor Sharing in Federated LoRA: A Rank-Aware Adaptive Approach

    Aug 10, 2026Xinyi Xu, Bingnan Xiao, Shuang Qin +2LLM Fine-TuningLow-Rank Adaptation

    University of Electronic Science and Technology of China · Fudan University · Singapore University of Technology and Design

  13. Label Granularity Skew in Federated Learning with Hierarchical Image Classification

    Aug 10, 2026Jaeheon Kim, Hokeun Kim, Bong Jun ChoiHierarchical ClassificationFederated Fine-Tuning

    School of Computer Science and Engineering, Soongsil University, Seoul, Republic of Korea · School of Computing and Augmented Intelligence, Arizona State University, Tempe, United States

  14. EFFEKT: Efficient Federated Knowledge Transfer to Foundation Models

    Aug 8, 2026Matteo Caligiuri, Francesco Barbato, Pietro Zanuttigh +1Federated Knowledge DistillationCross-Architecture Knowledge Distillation

    Department of Electrical & Computer Engineering, Northeastern University, Boston (MA), United States · Department of Information Engineering, University of Padua, Padua, Italy

  15. On the Effectiveness of Adaptation Strategies for VLM-Based Federated Learning in Remote Sensing

    Aug 5, 2026Simon Lösche, Barış Büyüktaş, Mathis Adler +3Remote Sensing Image UnderstandingPrompt Learning

    BIFOLD - Berlin Institute for the Foundations of Learning and Data, 10587 Berlin, Germany · Technische Universität Berlin, 10623 Berlin, Germany · Orion Lab, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15772 Athens, Greece +2

  16. Personalized Federated Sparse Adaptation of Time-Series Foundation Models

    Aug 5, 2026Priyanka Nihalchandani, Naman Srivastava, Varun Ojha +1Mixture of ExpertsTime Series Forecasting

    Robert Bosch Centre for Cyber-Physical Systems, Indian Institute of Science, Bengaluru, India · School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom

  17. FraQ: Efficient Coordinate-Space Recompression for Federated Low-Rank Adaptation

    Aug 4, 2026Shenghui Li, Thiemo VoigtFederated Learning AggregationCommunication-Efficient Distributed Training

    Uppsala University, Uppsala, Sweden · Research Institutes of Sweden, Stockholm, Sweden

  18. MineGrad: Gradient Inversion Attacks on LoRA Fine-Tuning

    Aug 2, 2026Hasin Us Sami, Swapneel Sen, Basak GulerGradient Inversion AttacksPrivacy Leakage in Language Models

    University of California, Riverside, CA

  19. FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting

    Aug 2, 2026Amit Sharma, Nitin Auluck, Akramul AzimTime Series ForecastingFederated Fine-Tuning

    Indian Institute of Technology Ropar, Punjab, India · Ontario Tech University, Oshawa, Canada

  20. Federated Foundation Models Fine-Tuning with Heterogeneous Compressed Clients

    Jul 31, 2026Shengkun Zhu, Jinshan Zeng, Zhihua Allen-Zhao +5Federated Learning AggregationFederated Fine-Tuning

    The Hong Kong Polytechnic University · OceanBase, Ant Group · School of Management, Xi’an Jiaotong University +3

  21. TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement

    Jul 30, 2026Cheng WeiBackdoor AttacksBackdoor Attacks in Federated Learning

    Honor Device Co., Ltd., Shenzhen, China

  22. FedWeave: Rethinking the Unit of Specialization in Heterogeneous Federated MoE-LoRA

    Jul 29, 2026Donghang Duan, Xu Zheng, Lizong Zhang +2Federated Learning AggregationLLM Routing

    University of Electronic Science and Technology of China · 2Zhejiang University

  23. Three-Pronged Spectral Control for Federated Parameter Efficient Fine Tuning

    Jul 23, 2026Shiva Raj Pokhrel, Dipsan Bhattarai, Anwar WalidFederated Learning AggregationNuclear Norm Minimization

    School of IT, Deakin University, Australia · Columbia University, USA

  24. Federated Lightweight Fine-Tuning

    Jul 20, 2026Radhakrishna Achanta, Will ReedFederated Learning AggregationFederated Fine-Tuning

    Cisco Systems Inc., USA

  25. Dysco: Dynamic Subspace Boosting to Mitigate LoRA Interference in Federated Learning

    Jul 15, 2026Haobo Zhang, Jiankun Wang, Suraj Rajendran +5Subspace OptimizationFederated Learning Aggregation

    University of Michigan · Cornell University · University of Pennsylvania

  26. Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning

    Jul 13, 2026Jing Liu, Chenxuanyin Zou, Jiayang Ren +5Multimodal Federated LearningGenerative Replay

    Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada · College of Future Information Technology, Fudan University, Shanghai 200433, China · Department of Chemical and Biological Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada +3

  27. PFAdapter: Hierarchical LoRA Decomposition for Personalized Federated MLLMs

    Jul 13, 2026Jing Liu, Kun Yang, Yan Wang +5Multimodal Federated LearningFederated Learning Aggregation

    College of Future Information Technology, Fudan University, Shanghai 200433, China · Division of Natural and Applied Sciences, Duke Kunshan University, Suzhou 215316, China · Department of Electrical and Computer Engineering, The University of British Columbia, BC V6T 1Z4, Canada +8

  28. Subspace-Constrained Federated Learning with Low-Rank Adaptation

    Jun 21, 2026Neranjan Senarath, Rohit Muralitharan, Sadia AsifFederated Learning AggregationNon-IID Federated Learning

    Department of ECSE Rensselaer Polytechnic Institute Troy, NY 12180, USA · Department of Computer Science Rensselaer Polytechnic Institute Troy, NY 12180, USA

  29. PreLort: Prefix-Nested LoRA for Federated Fine-Tuning under Rank Heterogeneity

    Jun 14, 2026Muhammad Waseem, Nurbek Tastan, Andrej Jovanovic +4Federated Learning AggregationLLM Fine-Tuning

    1MBZUAI, UAE · University of Cambridge, UK · 3Flower Labs, UK +1

  30. Conflict-Aware Federated Fine-Tuning of Large Language Models with Mixture-of-Experts

    Jun 14, 2026Yijun Lu, Zihan Fang, Pengpeng Qiao +6Federated Learning AggregationLLM Fine-Tuning

    Waseda University, Tokyo, Japan · City University of Hong Kong, China · Institute of Science Tokyo, Tokyo, Japan +4