mlsanityData to 14 Oct 2026

Federated Split Learning with Improved Communication and Storage Efficiency

arXiv 2507.15816 ↗First posted 21 Jul 2025cs.LG cs.IT cs.NIcited by 3

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Abstract (arXiv)

Federated learning (FL) is one of the popular distributed machine learning (ML) solutions but incurs significant communication and computation costs at edge devices. Federated split learning (FSL) can train sub-models in parallel and reduce the computational burden of edge devices by splitting the model architecture. However, it still requires a high communication overhead due to transmitting the smashed data and gradients between clients and the server in every global round. Furthermore, the server must maintain separate partial models for every client, leading to a significant storage requirement. To address these challenges, this paper proposes a novel communication and storage efficient federated split learning method, termed CSE-FSL, which utilizes an auxiliary network to locally update the weights of the clients while keeping a single model at the server, hence avoiding frequent transmissions of gradients from the server and greatly reducing the storage requirement of the server. Additionally, a new model update method of transmitting the smashed data in selected epochs can reduce the amount of smashed data sent from the clients. We provide a theoretical analysis of CSE-FSL, rigorously guaranteeing its convergence under non-convex loss functions. The extensive experimental results further indicate that CSE-FSL achieves a significant communication reduction over existing FSL solutions using real-world FL tasks.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 36 of its 60 references that are in the map. First posted on arXiv 21 Jul 2025.

Builds on

36 of 60 references.

References · 36

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2021Convex optimizationcited by 2,515
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  5. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  6. 2020Adaptive Federated Optimizationcited by 1,046
  7. 2018LEAF: A Benchmark for Federated Settingscited by 792
  8. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  9. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 478
  10. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 395
  11. 2019Error Feedback Fixes SignSGD and other Gradient Compression Schemescited by 310
  12. 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
24 more
  1. 2018Distributed learning of deep neural network over multiple agentscited by 279
  2. 2018ATOMO: Communication-efficient Learning via Atomic Sparsificationcited by 136
  3. 2020Unleashing the Tiger: Inference Attacks on Split Learningcited by 105
  4. 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
  5. 2020Acceleration for Compressed Gradient Descent in Distributed and Federated Optimizationcited by 90
  6. 2019Decoupled Greedy Learning of CNNscited by 78
  7. 2019Detailed comparison of communication efficiency of split learning and federated learningcited by 77
  8. 2019Split Learning for collaborative deep learning in healthcarecited by 73
  9. 2020End-to-End Evaluation of Federated Learning and Split Learning for Internet of Thingscited by 64
  10. 2020Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?cited by 50
  11. 2020Design and Analysis of Uplink and Downlink Communications for Federated Learningcited by 48
  12. 2020Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networkscited by 28
  13. 2020FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networkscited by 23
  14. 2023Privacy and Efficiency of Communications in Federated Split Learningcited by 21
  15. 2023Communication-Efficient Split Learning via Adaptive Feature-Wise Compressioncited by 18
  16. 2021Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Trainingcited by 17
  17. 2024ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devicescited by 16
  18. 2022Random Orthogonalization for Federated Learning in Massive MIMO Systemscited by 14
  19. 2023Communication and Storage Efficient Federated Split Learningcited by 11
  20. 2020SplitEasy: A Practical Approach for Training ML models on Mobile Devicescited by 10
  21. 2023Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learningcited by 9
  22. 2020Multiple Classification with Split Learningcited by 6
  23. 2021Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challengescited by 4
  24. 2024Efficient Model Compression for Hierarchical Federated Learningcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2026Lean Clients, Full Accuracy: Hybrid Zeroth- and First-Order Split Federated Learningcited by 2
  2. 2025Accelerating Wireless Distributed Learning via Hybrid Split and Federated Learning Optimizationcited by 0
  3. 2026Momentum-Guided Federated Split Distillation for Personalized Temporal Edge Intelligencecited by 0