arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2001.11342eess.SPcs.LGstat.ML

D2D-Enabled Data Sharing for Distributed Machine Learning at Wireless Network Edge

School of Information Engineering, Guangdong University of Technology(广东工业大学信息工程学院);Future Network of Intelligence Institute (FNii) and the School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen(香港中文大学(深圳)未来智能网络研究院及理工学院);The Chinese University of Hong Kong, Shenzhen(香港中文大学(深圳))

Xiaoran Cai, Xiaopeng Mo, Junyang Chen, Jie Xu

英文摘要:

Mobile edge learning is an emerging technique that enables distributed edge devices to collaborate in training shared machine learning models by exploiting their local data samples and communication and computation resources. To deal with the straggler dilemma issue faced in this technique, this paper proposes a new device to device enabled data sharing approach, in which different edge devices share their data samples among each other over communication links, in order to properly adjust their computation loads for increasing the training speed. Under this setup, we optimize the radio resource allocation for both data sharing and distributed training, with the objective of minimizing the total training delay under fixed numbers of local and global iterations. Numerical results show that the proposed data sharing design significantly reduces the training delay, and also enhances the training accuracy when the data samples are non independent and identically distributed among edge devices.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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