Split Federated Learning

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Overview

Split federated learning partitions a model so clients process private data locally and send intermediate activations to a server, which computes the remaining layers and returns gradients. Research studies cut-layer selection, client and server resource allocation, and communication-saving methods such as quantization, sparsification, and activation compression, alongside personalization for non-IID data and large-model fine-tuning. Privacy and security work examines leakage from intermediate representations and attacks such as reconstruction and poisoning, using tools including differential privacy, representation alignment, and robust training. A central tension remains: moving more computation off devices can reduce their resource burden, but split depth and exchanged representations affect accuracy, communication, and privacy, so these trade-offs often need to be adapted to each client and task.

Latest papers

CardsList

37 papers3 papers in the last four weeks, down 40% on the four weeks before. 0.0% of all new papers.

  1. Split Federated Unlearning

    Oct 27, 2026Jia Chen, Yecheng Lv, Xinrong Hu +2Federated UnlearningSplit Federated Learning

    School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, China · Engineering Research Center of Hubei Province for Clothing Information, Wuhan Textile University, Wuhan, China · Australian Institute for Machine Learning (AIML), Adelaide University, Adelaide, Australia

  2. CoASFL: Coupled Consistency Optimization for Asynchronous Split Federated Learning

    Oct 19, 2026Junhao Cheng, Yunming Liao, Hongli Xu +2Split Federated LearningFederated Learning

    School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China, 230027 · Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, China, 215123

  3. Importance-Aware Feature Sparsification for Wireless Split Learning

    Sep 30, 2026Bumjun Kim, Yoon Huh, Wan ChoiCommunication-Efficient Distributed TrainingSplit Federated Learning

    Department of Electrical and Computer Engineering, Seoul National University (SNU), and the Institute of New Media and Communications, SNU, Seoul 08826, Korea

  4. CoeF-SFL: Preserving Collaborative Server-Client Learning with Enhanced Communication Efficiency

    Sep 28, 2026Junwoo Bae, Jin-Hyun AhnCommunication-Efficient Distributed TrainingSplit Federated Learning

    Myongji University

  5. Latency-Aware Client Assignment for Parallel Split Learning With Global Sampling

    Sep 26, 2026Mohammad Kohankhaki, Valentin Rentschler, Anke SchmeinkSplit Federated LearningCombinatorial Optimization

    Chair of Information Theory and Data Analytics, RWTH Aachen University, Kopernikusstr. 16, 52074 Aachen, Germany · Cluster of Excellence CARE, TU Dresden and RWTH Aachen, Germany

  6. 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

  7. 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

  8. USPLIT-VQA: U-Shaped Split Learning for Visual Question Answering with Contribution-Aware Weighted Aggregation

    Sep 10, 2026Md Khalid Syfullah, Alvi Ataur KhalilVisual Question AnsweringFederated Learning Aggregation

    Transformative Innovation for Trustworthy AI and Network Security (TITANS) Lab, Computer Science, Southern Illinois University Carbondale, USA

  9. 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

  10. 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

  11. 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

  12. Efficient Resource Optimization for Split Federated Learning

    Aug 18, 2026Wei Wei, Xianhao ChenSplit Federated Learning

    Department of Electrical and Computer Engineering, the University of Hong Kong, Pok Fu Lam, Hong Kong SAR, 999077

  13. 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

  14. TOFD: Target-Oriented Feature Decoupling against Poisoning Attacks in Split Federated Learning

    Aug 7, 2026Yuhan Xie, Jingrong Huang, Chen LyuAdversarial RobustnessSplit Federated Learning

    Shanghai University of Finance and Economics MoE Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai, China

  15. GQ-FSL: Green Quantized Federated Split Learning

    Jul 31, 2026Idan Roth, Lutz LampeMixed-Precision QuantizationEnergy-Efficient ML

    The University of British Columbia, Vancouver, BC, Canada

  16. BettiSplit: Topology-Guided Privacy-Aware Split Learning Against Feature Inversion and Gradient Leakage

    Jul 27, 2026Akarsh K. Nair, Muhammad Arifur Rahman, David Brown +1Gradient Inversion AttacksSplit Federated Learning

    Department of Computer Science Nottingham Trent University Nottingham, United Kingdom · Information and Computer Science Department SDAIA–KFUPM Joint Research Center for AI Interdisciplinary Research Center for Biosystems and Machines King Fahd University of Petroleum & Minerals Dhahran 31261, Saudi Arabia

  17. Adaptive Joint Compression and Synchronisation in Federated Split Learning for IoT Rainfall Prediction

    Jun 23, 2026Wenjie Ding, Yi Sin Lin, Jiale Liu +8Edge ComputingSplit Federated Learning

    School of Computing · Newcastle University · Newcastle upon Tyne, United Kingdom

  18. QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning

    Jun 1, 2026Nazmus Shakib Shadin, Xinyue Zhang, Jingyi Wang +1Reinforcement LearningDeep Q-Learning

    Department of Computer Science, Kennesaw State University, Marietta, GA, 30060 USA · Department of Computer Science, San Francisco State University, San Francisco, CA, 94132 USA · Department of Electrical and Computer Engineering, University of Houston, Houston, TX, 77204 USA

  19. Separate Aggregation of Split Network for Personalized Federated Learning

    May 26, 2026Yunseok Kang, Jaeyoung SongFederated Learning AggregationSplit Federated Learning

    Department of Electronics Engineering, Pusan National University, Republic of Korea

  20. BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregation

    May 17, 2026Yuhan Xie, Chen Lyu, Jingrong HuangFederated Learning AggregationSplit Federated Learning

    Shanghai University of Finance and Economics, China · MoE Key Laboratory of Interdisciplinary Research of Computation and Economics, China.

  21. TSFLora: Token-Compressed Split Fine-Tuning for Wireless Edge Networks

    May 17, 2026Xianke Qiang, Zheng Chang, Li Wang +1Communication-Efficient Distributed TrainingEdge Computing

    School of Computer Science and Engineering, UESTC, 611731 Chengdu, China · School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China. · Center for Intelligent Networking and Communications, UESTC, 611731 Chengdu, China.

  22. SplitFed-CL: A Split Federated Co-Learning Framework for Medical Image Segmentation with Inaccurate Labels

    May 11, 2026Zahra Hafezi Kafshgari, Hadi Hadizadeh, Parvaneh SaeediSplit Federated LearningNoisy-Label Learning

    School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada

  23. HARMONY: Bridging the Personalization-Generalization Gap by Mitigating Representation Skew in Heterogeneous Split Federated Learning

    May 8, 2026Jiseok Youn, You Rim Choi, Goodsol Lee +3Meta-LearningOOD Generalization

    Department of ECE and INMC, Seoul National University, Seoul, South Korea · Graduate School of Data Science, Seoul National University, Seoul, South Korea · Department of Computer Science, University of Colorado Boulder, CO, USA

  24. MuCALD-SplitFed: Causal-Latent Diffusion for Privacy-Preserving Multi-Task Split-Federated Medical Image Segmentation

    May 4, 2026Chamani Shiranthika, Hadi Hadizadeh, Parvaneh SaeediLatent Diffusion ModelsSplit Federated Learning

    School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada

  25. SplitFT: An Adaptive Federated Split Learning System For LLMs Fine-Tuning

    Apr 29, 2026Yimeng Shan, Zhaorui Zhang, Sheng Di +3Communication-Efficient Distributed TrainingLLM Fine-Tuning

    Hong Kong Polytechnic University Hong Kong · Argonne National Labratory USA · University of California, Merced USA +1

  26. BVFLMSP : Bayesian Vertical Federated Learning for Multimodal Survival with Privacy

    Apr 2, 2026Abhilash Kar, Basisth Saha, Tanmay Sen +1Multimodal Survival AnalysisFederated Learning

    SQC and OR Unit, Indian Statistical Institute, Kolkata, India · Department of Statistics, Presidency University, Kolkata, India

  27. GAPSL: A Gradient-Aligned Parallel Split Learning over Data-Heterogeneous Edge Computing Systems

    Mar 19, 2026Zheng Lin, Ons Aouedi, Zihan Fang +4Split Federated LearningGradient Alignment

    Department of Electrical and Computer Engineering, University of Hong Kong, Pok Fu Lam, Hong Kong, China · Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, L-1855 Luxembourg, Luxembourg · Hong Kong JC STEM Lab of Smart City and Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong SAR, China +2

  28. HO-SFL: Hybrid-Order Split Federated Learning with Backprop-Free Clients and Dimension-Free Aggregation

    Mar 16, 2026Qiyuan Chen, Xian Wu, Yi Wang +1Communication-Efficient Distributed TrainingZeroth-Order Optimization

    Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong SAR, China.

  29. Split Federated Learning Architectures for High-Accuracy and Low-Delay Model Training

    Mar 9, 2026Yiannis Papageorgiou, Yannis Thomas, Ramin Khalili +1Split Federated LearningFederated Learning

    Department of Informatics, Athens University of Economics and Business, Athens, Greece · Huawei Technologies Duesseldorf Gmbh, Munich, Germany

  30. Semantic Communication-Enhanced Split Federated Learning for Vehicular Networks: Architecture, Challenges, and Case Study

    Mar 5, 2026Lu Yu, Zheng Chang, Ying-Chang LiangSplit Federated LearningFederated Learning

    School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China · Faculty of Information Technology, University of Jyväskylä, FIN-40014 Jyväskylä, Finland · Center for Intelligent Networking and Communications (CINC), University of Electronic Science and Technology of China, 611731 Chengdu, China