Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks

Howard H. Yang, Ahmed Arafa, Tony Q.S. Quek, H. Vincent Poor

研究成果: Conference contribution

11 引文 斯高帕斯(Scopus)

摘要

Federated learning (FL) is a machine learning model that preserves data privacy in the training process. Specifically, FL brings the model directly to the user equipments (UEs) for local training, where an edge server periodically collects the trained parameters to produce an improved model and sends it back to the UEs. However, since communication usually occurs through a limited spectrum, only a portion of the UEs can update their parameters upon each global aggregation. As such, new scheduling algorithms have to be engineered to facilitate the full implementation of FL. In this paper, based on a metric termed the age of update (AoU), we propose a scheduling policy by jointly accounting for the staleness of the received parameters and the instantaneous channel qualities to improve the running efficiency of FL. The proposed algorithm has low complexity and its effectiveness is demonstrated by Monte Carlo simulations.

原文English
主出版物標題2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面8743-8747
頁數5
ISBN(電子)9781509066315
DOIs
出版狀態Published - 2020 五月
事件2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
持續時間: 2020 五月 42020 五月 8

出版系列

名字ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2020-May
ISSN(列印)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
國家Spain
城市Barcelona
期間20-05-0420-05-08

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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