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Federated learning is a privacy-preserving Machine Learning technique that could unlock the participation of a large number of distributed collaborating clients fostering the development of effective models that need personal and sensitive data to be trained and tested. In this paper, we present a preliminary evaluation of such cutting-edge techniques that may leverage the future adoption of innovative wearable devices. Experimental activities concern the recognition of motor imagery (MI) through EEG signals to build brain-computer interfaces (BCIs).

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Federated Learning for Training Brain-Computer Interfaces

  • 会议论文

  • 发表日期：

    2025年

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  • Alba Amato,
  • Vittorio Belardo,
  • Domenico Di Sivo,
  • Pietro Fusco,
  • Salvatore Venticinque

摘要

Federated learning is a privacy-preserving Machine Learning technique that could unlock the participation of a large number of distributed collaborating clients fostering the development of effective models that need personal and sensitive data to be trained and tested. In this paper, we present a preliminary evaluation of such cutting-edge techniques that may leverage the future adoption of innovative wearable devices. Experimental activities concern the recognition of motor imagery (MI) through EEG signals to build brain-computer interfaces (BCIs).