mlsanityData to 14 Oct 2026

Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions

arXiv 2207.09611 ↗First posted 20 Jul 2022cs.LG cs.DCcited by 14

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Abstract (arXiv)

Federated learning (FL) and split learning (SL) are two emerging collaborative learning methods that may greatly facilitate ubiquitous intelligence in Internet of Things (IoT). Federated learning enables machine learning (ML) models locally trained using private data to be aggregated into a global model. Split learning allows different portions of an ML model to be collaboratively trained on different workers in a learning framework. Federated learning and split learning, each has unique advantages and respective limitations, may complement each other toward ubiquitous intelligence in IoT. Therefore, combination of federated learning and split learning recently became an active research area attracting extensive interest. In this article, we review the latest developments in federated learning and split learning and present a survey on the state-of-the-art technologies for combining these two learning methods in an edge computing-based IoT environment. We also identify some open problems and discuss possible directions for future research in this area with a hope to further arouse the research community's interest in this emerging field.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 71 of its 112 references that are in the map. First posted on arXiv 20 Jul 2022.

Builds on

71 of 112 references.

References · 71

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  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
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38 more
  1. 2019Fair Resource Allocation in Federated Learningcited by 394
  2. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  3. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  4. 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
  5. 2018Distributed learning of deep neural network over multiple agentscited by 279
  6. 2019Asynchronous Federated Optimizationcited by 267
  7. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  8. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 198
  9. 2021Federated Learning with Buffered Asynchronous Aggregationcited by 190
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  11. 2018Expanding the Reach of Federated Learning by Reducing Client Resource Requirementscited by 163
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  14. 2018Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networkscited by 142
  15. 2020Unleashing the Tiger: Inference Attacks on Split Learningcited by 105
  16. 2019Continual Learning via Neural Pruningcited by 101
  17. 2021CRFL: Certifiably Robust Federated Learning against Backdoor Attackscited by 91
  18. 2021Label Leakage and Protection in Two-party Split Learningcited by 90
  19. 2020VAFL: a Method of Vertical Asynchronous Federated Learningcited by 87
  20. 2020Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approachcited by 81
  21. 2019Detailed comparison of communication efficiency of split learning and federated learningcited by 77
  22. 2021Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challengescited by 76
  23. 2022Split Learning over Wireless Networks: Parallel Design and Resource Managementcited by 75
  24. 2019Byzantine-Robust Federated Machine Learning through Adaptive Model Averagingcited by 70
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  26. 2020End-to-End Evaluation of Federated Learning and Split Learning for Internet of Thingscited by 64
  27. 2021Provably Secure Federated Learning against Malicious Clientscited by 63
  28. 2017Mobile Edge Computing: A Survey on Architecture and Computation Offloadingcited by 53
  29. 2021UnSplit: Data-Oblivious Model Inversion, Model Stealing, and Label Inference Attacks Against Split Learningcited by 52
  30. 2020Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?cited by 50
  31. 2020To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devicescited by 46
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  37. 2021Practical Defences Against Model Inversion Attacks for Split Neural Networkscited by 29
  38. 2019Shielding Collaborative Learning: Mitigating Poisoning Attacks through Client-Side Detectioncited by 26

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Citing papers per year: 2023: 5; 2024: 4; 2025: 3; 2026: 2.

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  1. 2024Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunitiescited by 26
  2. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  3. 2023Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learningcited by 9
  4. 2023Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informaticscited by 5
  5. 2023Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attackscited by 4
  6. 2023PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clientscited by 3
  7. 2023A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computingcited by 3
  8. 2024CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authenticationcited by 2
  9. 2024Federated Learning with Flexible Architecturescited by 1
  10. 2025A Novel Collaborative Framework for Efficient Synchronization in Split Federated Learning over Wireless Networkscited by 1
  11. 2025Oops!... They Stole it Again: Attacks on Split Learningcited by 1
  12. 2026Hybrid Federated and Split Learning for Privacy Preserving Clinical Prediction and Treatment Optimizationcited by 1
2 more
  1. 2025Enhancing Split Learning with Sharded and Blockchain-Enabled SplitFed Approachescited by 0
  2. 2026StreamSplit: Continuous Audio Representation Learning via Uncertainty-Guided Adaptive Splittingcited by 0