Personalizing Federated Learning with Over-The-Air Computations

Zihan Chen, Zeshen Li, Howard H. Yang, Tony Q.S. Quek

研究成果: Conference contribution

3 引文 斯高帕斯(Scopus)

摘要

Federated edge learning is a promising technology to deploy intelligence at the edge of wireless networks in a privacy-preserving manner. Under such a setting, multiple clients collaboratively train a global generic model under the coordination of an edge server. But the training efficiency is often hindered by challenges arising from limited communication and data heterogeneity. In this paper, we present a distributed training paradigm that employs analog over-the-air computation to alleviate the communication bottleneck. Additionally, we leverage a bi-level optimization framework to personalize the federated learning model so as to cope with the data heterogeneity issue. As a result, it enhances the generalization and robustness of each client's local model. We elaborate on the model training procedure and its advantages over conventional frameworks. We provide a convergence analysis that theoretically demonstrates the training efficiency. We also conduct extensive experiments to validate the efficacy of the proposed framework.

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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