Split Federated Learning

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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

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

Guarding the Middle: Protecting Intermediate Representations in Federated Split Learning
Feb 19, 2026Obaidullah Zaland, Sajib Mistry, Monowar BhuyanTraining Data Reconstruction Attacks Privacy-Preserving ML
Department of Computing Science, Umeå Unviersity, Umeå, SE-90187, Sweden · Curtin University, Bentley WA 6102, Australia
ASFL: An Adaptive Model Splitting and Resource Allocation Framework for Split Federated Learning
Feb 19, 2026Chuiyang Meng, Ming Tang, Vincent W. S. WongSplit Federated Learning Energy-Efficient ML
Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, Canada · Department of Computer Science and Engineering, Southern University of Science and Technology, China
Edge Learning via Federated Split Decision Transformers for Metaverse Resource Allocation
Feb 18, 2026Fatih Temiz, Shavbo Salehi, Melike Erol-KantarciWireless Resource Allocation Federated RL
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada
Hybrid Federated and Split Learning for Privacy Preserving Clinical Prediction and Treatment Optimization
Feb 17, 2026Farzana Akter, Rakib Hossain, Deb Kanna Roy Toushi +3Uplift Modeling Privacy-Preserving ML
Dept. of Information Technology, Washington University of Science and Technology, Alexandria, VA, USA · Cognitive Links LLC, Maryland, USA
ELSA: Efficient LLM-Centric Split Aggregation for Privacy-Aware Hierarchical Federated Learning over the Network Edge
Jan 20, 2026Xiaohong Yang, Tong Xie, Minghui Liwang +6Federated Fine-Tuning Federated Learning
the School of Informatics, Xiamen University, Fujian, China · the National University of Singapore · the Department of Control Science and Engineering, the National Key Laboratory of Autonomous Intelligent Unmanned Systems, and also with Frontiers Science Center for Intelligent Autonomous Systems, Ministry of Education, Tongji University, Shanghai, China +2
FlexP-SFT: A Flexible Aggregation-Free Framework for On-Device Personalized Split Federated Fine-Tuning of LLMs
Aug 14, 2025Jiaxiang Geng, Tianjun Yuan, Pengchao Han +3Communication-Efficient Distributed Training LLM Fine-Tuning
Duke Kunshan University, Suzhou, China · Guangdong University of Technology, Guangzhou, China · Jade Bird Fire (JBF), China +1
How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?
Dec 10, 2024Joohyung Lee, Jungchan Cho, Wonjun Lee +2Mechanism Design Split Federated Learning
School of Computing, Gachon University, Seongnam 13120, Rep. of Korea · School of Cybersecurity, Korea University, Seoul, Rep. of Korea · Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544 USA

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Guarding the Middle: Protecting Intermediate Representations in Federated Split Learning

Obaidullah Zaland, Sajib Mistry, Monowar Bhuyan

Department of Computing Science, Umeå Unviersity, Umeå, SE-90187, Sweden · Curtin University, Bentley WA 6102, Australia

Training Data Reconstruction Attacks Privacy-Preserving ML Split Federated Learning Differentially Private Federated Learning

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Big data scenarios, where massive, heterogeneous datasets are distributed across clients, demand scalable, privacy-preserving learning methods. Federated learning (FL) enables decentralized training of machine learning (ML) models across clients without data centralization. Decentralized training, however, introduces a computational burden on client devices. U-shaped federated split learning (UFSL) offloads a fraction of the client computation to the server while keeping both data and labels on the clients' side. However, the intermediate representations (i.e., smashed data) shared by clients with the server are prone to exposing clients' private data. To reduce exposure of client data through intermediate data representations, this work proposes k-anonymous differentially private UFSL (KD-UFSL), which leverages privacy-enhancing techniques such as microaggregation and differential privacy to minimize data leakage from the smashed data transferred to the server. We first demonstrate that an adversary can access private client data from intermediate representations via a data-reconstruction attack, and then present a privacy-enhancing solution, KD-UFSL, to mitigate this risk. Our experiments indicate that, alongside increasing the mean squared error between the actual and reconstructed images by up to 50% in some cases, KD-UFSL also decreases the structural similarity between them by up to 40% on four benchmarking datasets. More importantly, KD-UFSL improves privacy while preserving the utility of the global model. This highlights its suitability for large-scale big data applications where privacy and utility must be balanced.