mlsanityData to 14 Oct 2026

Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Things

arXiv 2311.04944 ↗First posted 8 Nov 2023cs.LG cs.AI cs.CRcited by 3

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Abstract (arXiv)

In the realm of the Internet of Things (IoT), deploying deep learning models to process data generated or collected by IoT devices is a critical challenge. However, direct data transmission can cause network congestion and inefficient execution, given that IoT devices typically lack computation and communication capabilities. Centralized data processing in data centers is also no longer feasible due to concerns over data privacy and security. To address these challenges, we present an innovative Edge-assisted U-Shaped Split Federated Learning (EUSFL) framework, which harnesses the high-performance capabilities of edge servers to assist IoT devices in model training and optimization process. In this framework, we leverage Federated Learning (FL) to enable data holders to collaboratively train models without sharing their data, thereby enhancing data privacy protection by transmitting only model parameters. Additionally, inspired by Split Learning (SL), we split the neural network into three parts using U-shaped splitting for local training on IoT devices. By exploiting the greater computation capability of edge servers, our framework effectively reduces overall training time and allows IoT devices with varying capabilities to perform training tasks efficiently. Furthermore, we proposed a novel noise mechanism called LabelDP to ensure that data features and labels can securely resist reconstruction attacks, eliminating the risk of privacy leakage. Our theoretical analysis and experimental results demonstrate that EUSFL can be integrated with various aggregation algorithms, maintaining good performance across different computing capabilities of IoT devices, and significantly reducing training time and local computation overhead.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 29 of its 46 references that are in the map. First posted on arXiv 8 Nov 2023.

Builds on

29 of 46 references.

References · 29

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  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
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17 more
  1. 2022FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correctioncited by 181
  2. 2019Federated Learning for Emoji Prediction in a Mobile Keyboardcited by 118
  3. 2020Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Thingscited by 111
  4. 2020Unleashing the Tiger: Inference Attacks on Split Learningcited by 105
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  7. 2022Split Learning over Wireless Networks: Parallel Design and Resource Managementcited by 75
  8. 2020End-to-End Evaluation of Federated Learning and Split Learning for Internet of Thingscited by 64
  9. 2020Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?cited by 50
  10. 2021Federated Learning Meets Natural Language Processing: A Surveycited by 43
  11. 2020Advancements of federated learning towards privacy preservation: from federated learning to split learningcited by 34
  12. 2020FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networkscited by 23
  13. 2019Beyond Human-Level Accuracy: Computational Challenges in Deep Learningcited by 19
  14. 2021Federated Learning over Wireless IoT Networks with Optimized Communication and Resourcescited by 13
  15. 2021Communication and Computation Reduction for Split Learning using Asynchronous Trainingcited by 11
  16. 2020Performance Analysis and Comparison of Machine and Deep Learning Algorithms for IoT Data Classificationcited by 9
  17. 2022Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learningcited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024A Multivocal Literature Review on Privacy and Fairness in Federated Learningcited by 0
  2. 2025HLF-FSL. A Decentralized Federated Split Learning Solution for IoT on Hyperledger Fabriccited by 0
  3. 2026Momentum-Guided Federated Split Distillation for Personalized Temporal Edge Intelligencecited by 0