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2023

Optimal Resource Allocation for U-Shaped Parallel Split Learning

Lyu, Song, Lin, Zheng, Qu, Guanqiao et al.

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Split learning (SL) has emerged as a promising approach for model training without revealing the raw data samples from the data owners.

However, traditional SL inevitably leaks label privacy as the tail model (with the last layers) should be placed on the server.
To overcome this limitation, one promising solution is to utilize U-shaped architecture to leave both early layers and last layers on the user side.
In this paper, we develop a novel parallel U-shaped split learning and devise the optimal resource optimization scheme to improve the performance of edge networks.

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H. Peng, A.-H. Tsai, L.-C. Wang, and Z. Han, “LEOPARD: Parallel Optimal Deep Echo State Network Prediction Improves Service Coverage for UAV-Assisted Outdoor Hotspots,” IEEE Trans. Cogn. Commun. Netw. , vol. 8, no. 1, pp. 282–295, 2021
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