Improving the efficiency of split learning based on multi-user and parallel training

Xiaofeng Lu; Yinhui Li

doi:10.1117/12.3011831

8 November 2023 Improving the efficiency of split learning based on multi-user and parallel training

Xiaofeng Lu, Yinhui Li

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 129231W (2023) https://doi.org/10.1117/12.3011831
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

Federated learning has rapidly become a research hotspot in the field of distributed deep learning because raw data does not need to be shared. However, it is still characterized by problems in terms of privacy security and computing power. Split learning is a new distributed deep learning technology. By dividing the complete deep learning model, the client and server can participate in the training together to overcome some problems related to computing power and privacy and security. In this paper, the concept of split learning is introduced. This article introduces the use of multi-user and parallel training methods to improve the learning efficiency of split learning.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaofeng Lu and Yinhui Li "Improving the efficiency of split learning based on multi-user and parallel training", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 129231W (8 November 2023); https://doi.org/10.1117/12.3011831

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