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Critical ethical challenges, such as data privacy, algorithmic governance, and equitable access, are increasingly relevant in a digital world where sensitive personal or community data are collected centrally or stored in the cloud. This study presents a comparative analysis of Centralized Learning and Federated Learning for privacy-preserving machine learning, using healthcare sleep disorder detection as a case study. The methodology employs ballistocardiography (BCG) signals acquired via non-invasive sensors, illustrating a replicable workflow for distributed, privacy-preserving analysis. Both approaches are evaluated using Key Performance Indicators (KPIs) to assess accuracy, privacy preservation, and computational efficiency. Experimental results show that the federated model achieves 77.98% testing accuracy, slightly surpassing the centralized model (75.71%), with only 18.27 s per training round and 2.91 s for testing, demonstrating fast convergence and competitive performance while preserving data privacy.

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Enabling Privacy-Preserving Digital Well-Being Through Federated Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Pietro Fusco,
  • Palma Errico,
  • Domenico di Sivo,
  • Salvatore Venticinque

摘要

Critical ethical challenges, such as data privacy, algorithmic governance, and equitable access, are increasingly relevant in a digital world where sensitive personal or community data are collected centrally or stored in the cloud. This study presents a comparative analysis of Centralized Learning and Federated Learning for privacy-preserving machine learning, using healthcare sleep disorder detection as a case study. The methodology employs ballistocardiography (BCG) signals acquired via non-invasive sensors, illustrating a replicable workflow for distributed, privacy-preserving analysis. Both approaches are evaluated using Key Performance Indicators (KPIs) to assess accuracy, privacy preservation, and computational efficiency. Experimental results show that the federated model achieves 77.98% testing accuracy, slightly surpassing the centralized model (75.71%), with only 18.27 s per training round and 2.91 s for testing, demonstrating fast convergence and competitive performance while preserving data privacy.