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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728194417
DOIs
Publication statusPublished - 2021 Jun
Event2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Virtual, Online
Duration: 2021 Jun 142021 Jun 23

Publication series

Name2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings

Conference

Conference2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021
CityVirtual, Online
Period21-06-1421-06-23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management

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