mlsanityData to 7 Oct 2026

Communication and Computation Reduction for Split Learning using Asynchronous Training

arXiv 2107.09786 ↗First posted 20 Jul 2021cs.LGcited by 11

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Abstract (arXiv)

Split learning is a promising privacy-preserving distributed learning scheme that has low computation requirement at the edge device but has the disadvantage of high communication overhead between edge device and server. To reduce the communication overhead, this paper proposes a loss-based asynchronous training scheme that updates the client-side model less frequently and only sends/receives activations/gradients in selected epochs. To further reduce the communication overhead, the activations/gradients are quantized using 8-bit floating point prior to transmission. An added benefit of the proposed communication reduction method is that the computations at the client side are reduced due to reduction in the number of client model updates. Furthermore, the privacy of the proposed communication reduction based split learning method is almost the same as traditional split learning. Simulation results on VGG11, VGG13 and ResNet18 models on CIFAR-10 show that the communication cost is reduced by 1.64x-106.7x and the computations in the client are reduced by 2.86x-32.1x when the accuracy degradation is less than 0.5% for the single-client case. For 5 and 10-client cases, the communication cost reduction is 11.9x and 11.3x on VGG11 for 0.5% loss in accuracy.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 12 of its 19 references that are in the map. First posted on arXiv 20 Jul 2021.

Builds on

12 of 19 references.

References · 12

  1. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  3. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  4. 2020SplitFed: When Federated Learning Meets Split Learningcited by 308
  5. 2018Distributed learning of deep neural network over multiple agentscited by 277
  6. 2019The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networkscited by 241
  7. 2019Detailed comparison of communication efficiency of split learning and federated learningcited by 76
  8. 2020NoPeek: Information leakage reduction to share activations in distributed deep learningcited by 66
  9. 2020End-to-End Evaluation of Federated Learning and Split Learning for Internet of Thingscited by 64
  10. 2018Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communicationcited by 51
  11. 2020Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?cited by 50
  12. 2020SplitEasy: A Practical Approach for Training ML models on Mobile Devicescited by 10

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Cited by year

Citing papers per year: 2022: 3; 2023: 6; 2024: 0; 2025: 2.

11 papers.

  1. 2023Reducing Communication for Split Learning by Randomized Top-k Sparsificationcited by 22
  2. 2023Communication-Efficient Split Learning via Adaptive Feature-Wise Compressioncited by 18
  3. 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
  4. 2025Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approachcited by 7
  5. 2023SLPerf: a Unified Framework for Benchmarking Split Learningcited by 3
  6. 2023Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Thingscited by 3
  7. 2022PiPar: Pipeline Parallelism for Collaborative Machine Learningcited by 2
  8. 2023Defending Label Inference Attacks in Split Learning under Regression Settingcited by 2
  9. 2023Collaborative Distributed Machine Learningcited by 1
  10. 2025SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Frameworkcited by 1
  11. 2022Split Federated Learning on Micro-controllers: A Keyword Spotting Showcasecited by 0