Joint optimization of data sampling and user selection for federated learning in the mobile edge computing systems

Chenyuan Feng, Yidong Wang, Zhongyuan Zhao, Tony Q.S. Quek, Mugen Peng

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

1 Citation (Scopus)

Abstract

Federated learning is a model-level aggregation learning paradigm, which can generate high quality models without collecting the local private data of users. As a distributed coordination learning method, it can be deployed at the edge devices in mobile edge computing (MEC) systems, and provides an applicable solution of implementing network edge intelligence. However, the performance of federated learning cannot be guaranteed in the MEC systems, since the quality of local training data and wireless channels is not always satisfactory. To tackle with this problem, the joint optimization of data sampling and user selection is studied in this paper. First, to capture the key features of deploying federated learning in the MEC systems, we formulate an optimization problem to minimize the accuracy loss and cost, considering the computation and communication resource constraints. Then, an optimization algorithm is designed to jointly optimize the data sampling and user selection strategies, which can approach the stationary optimal solution efficiently. Finally, the numerical simulation and experiment results are provided to evaluate the performance of our proposed optimization scheme, which show that our proposed algorithm can significantly improve the performance of federated learning in the MEC systems.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728174402
DOIs
Publication statusPublished - 2020 Jun
Event2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Dublin, Ireland
Duration: 2020 Jun 72020 Jun 11

Publication series

Name2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings

Conference

Conference2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020
CountryIreland
CityDublin
Period20-06-0720-06-11

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Signal Processing
  • Information Systems and Management
  • Control and Optimization

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