DPP-Based Client Selection for Federated Learning with NON-IID DATA

Yuxuan Zhang, Chao Xu, Howard H. Yang, Xijun Wang, Tony Q.S. Quek

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

This paper proposes a client selection (CS) method to tackle the communication bottleneck of federated learning (FL) while concurrently coping with FL's data heterogeneity issue. Specifically, we first analyze the effect of CS in FL and show that FL training can be accelerated by adequately choosing participants to diversify the training dataset in each round of training. Based on this, we lever-age data profiling and determinantal point process (DPP) sampling techniques to develop an algorithm termed Federated Learning with DPP-based Participant Selection (FL-DP3S). This algorithm effectively diversifies the participants' datasets in each round of training while preserving their data privacy. We conduct extensive experiments to examine the efficacy of our proposed method. The results show that our scheme attains a faster convergence rate, as well as a smaller communication overhead than several baselines.

原文English
主出版物標題ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728163277
DOIs
出版狀態Published - 2023
事件48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece
持續時間: 2023 6月 42023 6月 10

出版系列

名字ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2023-June
ISSN(列印)1520-6149

Conference

Conference48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
國家/地區Greece
城市Rhodes Island
期間23-06-0423-06-10

All Science Journal Classification (ASJC) codes

  • 軟體
  • 訊號處理
  • 電氣與電子工程

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