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Federated Learning (FL) has emerged as a revolutionary paradigm in the context of artificial intelligence, enabling the training of models in a decentralized manner and ensuring data privacy and security. This paper aims to conduct an in-depth analysis of Federated Learning, exploring the fundamental concepts, benefits and challenges. Case studies in healthcare will be illustrated to demonstrate how FL can improve medical image analysis, monitoring via wearable devices, and drug discovery while preserving patient confidentiality. Finally, challenges related to data heterogeneity, security, and communication complexity, especially in edge computing and IoT environments, are examined and how they can be mitigated through the use of pattern. Additionally, a new Centralized Data Loader approach will be discussed.

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Federated Learning Framework for Privacy-Preserving AI in Healthcare

  • 会议论文

  • 发表日期：

    2025年

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  • Germano Ammirata,
  • Gennaro Junior Pezzullo,
  • Salvatore Contino,
  • Beniamino Di Martino,
  • Roberto Pirrone

摘要

Federated Learning (FL) has emerged as a revolutionary paradigm in the context of artificial intelligence, enabling the training of models in a decentralized manner and ensuring data privacy and security. This paper aims to conduct an in-depth analysis of Federated Learning, exploring the fundamental concepts, benefits and challenges. Case studies in healthcare will be illustrated to demonstrate how FL can improve medical image analysis, monitoring via wearable devices, and drug discovery while preserving patient confidentiality. Finally, challenges related to data heterogeneity, security, and communication complexity, especially in edge computing and IoT environments, are examined and how they can be mitigated through the use of pattern. Additionally, a new Centralized Data Loader approach will be discussed.