# Communication and Computation Reduction for Split Learning using Asynchronous Training

arXiv: https://arxiv.org/abs/2107.09786 · HTML: https://mlsanity.com/paper/2107.09786/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 20 Jul 2021.

## Builds on (most cited first, 12 of 12)

- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [SplitFed: When Federated Learning Meets Split Learning](https://mlsanity.com/paper/2004.12088/) (2020)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks](https://mlsanity.com/paper/1911.07135/) (2019)
- [Detailed comparison of communication efficiency of split learning and federated learning](https://mlsanity.com/paper/1909.09145/) (2019)
- [NoPeek: Information leakage reduction to share activations in distributed deep learning](https://mlsanity.com/paper/2008.09161/) (2020)
- [End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things](https://mlsanity.com/paper/2003.13376/) (2020)
- [Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication](https://mlsanity.com/paper/1805.08768/) (2018)
- [Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?](https://mlsanity.com/paper/2003.12365/) (2020)
- [SplitEasy: A Practical Approach for Training ML models on Mobile Devices](https://mlsanity.com/paper/2011.04232/) (2020)

## Built on by (most cited first, 11 of 11)

- [Reducing Communication for Split Learning by Randomized Top-k Sparsification](https://mlsanity.com/paper/2305.18469/) (2023)
- [Communication-Efficient Split Learning via Adaptive Feature-Wise Compression](https://mlsanity.com/paper/2307.10805/) (2023)
- [Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions](https://mlsanity.com/paper/2207.09611/) (2022)
- [Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approach](https://mlsanity.com/paper/2510.21155/) (2025)
- [SLPerf: a Unified Framework for Benchmarking Split Learning](https://mlsanity.com/paper/2304.01502/) (2023)
- [Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Things](https://mlsanity.com/paper/2311.04944/) (2023)
- [PiPar: Pipeline Parallelism for Collaborative Machine Learning](https://mlsanity.com/paper/2302.12803/) (2022)
- [Defending Label Inference Attacks in Split Learning under Regression Setting](https://mlsanity.com/paper/2308.09448/) (2023)
- [SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Framework](https://mlsanity.com/paper/2504.13479/) (2025)
- [Collaborative Distributed Machine Learning](https://mlsanity.com/paper/2309.16584/) (2023)
- [Split Federated Learning on Micro-controllers: A Keyword Spotting Showcase](https://mlsanity.com/paper/2210.01961/) (2022)

## 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.