Optimal Design of Hybrid Federated and Centralized Learning in the Mobile Edge Computing Systems

Wei Hong, Xueting Luo, Zhongyuan Zhao, Mugen Peng, Tony Q.S. Quek

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

It is a dilemma to balance the tradeoff between the computation efficiency and communication cost of deploying deep learning models in the mobile edge computing (MEC) systems, due to the isolation of collected data and computation capability. To solve this problem, a hybrid federated and centralized learning scheme is first proposed in this paper, where the learning model can be jointly generated based on the centralized learning model and the federated learning model. It can make full use of both the collected data of user terminals and power full computation capability of edge computing servers. Second, to guarantee the model accuracy with communication, computation, and data constraints, an optimization algorithm is designed to keep a sophisticated tradeoff of model accuracy and training cost. Finally, the experiment results base on the image data set are provided, which show that our proposed algorithm can significantly improve the model accuracy with low costs.

原文English
主出版物標題2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728194417
DOIs
出版狀態Published - 2021 6月
事件2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Virtual, Online
持續時間: 2021 6月 142021 6月 23

出版系列

名字2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings

Conference

Conference2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021
城市Virtual, Online
期間21-06-1421-06-23

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 電腦網路與通信
  • 硬體和架構
  • 資訊系統與管理

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