A Review and Experimental Evaluation on Split Learning

Zhanyi Hu, Tianchen Zhou, Bingzhe Wu, Cen Chen, Yanhao Wang

Abstract: Training deep learning models collaboratively on decentralized edge devices has attracted significant attention recently. The two most prominent schemes for this problem are Federated Learning (FL) and Split Learning (SL). Although there have been several surveys and experimental evaluations for FL in the literature, SL paradigms have not yet been systematically reviewed and evaluated. Due to the diversity of SL paradigms in terms of label sharing, model aggregation, cut layer selection, etc., the lack of a systematic survey makes it difficult to fairly and conveniently compare the performance of different SL paradigms. To address the above issue, in this paper, we first provide a comprehensive review for existing SL paradigms. Then, we implement several typical SL paradigms and perform extensive experiments to compare their performance in different scenarios on four widely used datasets. The experimental results provide extensive engineering advice and research insights for SL paradigms. We hope that our work can facilitate future research on SL by establishing a fair and accessible benchmark for SL performance evaluation.