Resource-efficient Parallel Split Learning in Heterogeneous Edge Computing

被引:0
作者
Zhang, Mingjin [1 ]
Cao, Jiannong [1 ]
Sahni, Yuvraj [1 ]
Chen, Xiangchun [1 ]
Jiang, Shan [1 ]
机构
[1] Hong Kong Polytech Univ, Hong Kong, Peoples R China
来源
2024 INTERNATIONAL CONFERENCE ON COMPUTING, NETWORKING AND COMMUNICATIONS, ICNC | 2024年
关键词
Edge Computing; Federated Learning; Edge AI; Task Scheduling;
D O I
10.1109/CNC59896.2024.10556386
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Edge AI has been recently proposed to facilitate the training and deployment of Deep Neural Network (DNN) models in proximity to the sources of data. To enable the training of large models on resource-constraint edge devices and protect data privacy, parallel split learning is becoming a practical and popular approach. However, current parallel split learning neglects the resource heterogeneity of edge devices, which may lead to the straggler issue. In this paper, we propose EdgeSplit, a novel parallel split learning framework to better accelerate distributed model training on heterogeneous and resource-constraint edge devices. EdgeSplit enhances the efficiency of model training on less powerful edge devices by adaptively segmenting the model into varying depths. Our approach focuses on reducing total training time by formulating and solving a task scheduling problem, which determines the most efficient model partition points and bandwidth allocation for each device. We employ a straightforward yet effective alternating algorithm for this purpose. Comprehensive tests conducted with a range of DNN models and datasets demonstrate that EdgeSplit not only facilitates the training of large models on resource-restricted edge devices but also surpasses existing baselines in performance.
引用
收藏
页码:794 / 798
页数:5
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