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Federated Learning (FL) has emerged as a revolutionary machine learning paradigm that enables distributed model training without centralizing sensitive data, offering significant benefits for Internet of Things (IoT) applications. This paper explores the integration of FL within IoT environments, highlighting the challenges, recent advancements, and future directions. The study emphasizes privacy preservation, data heterogeneity, and communication efficiency while offering insights into emerging trends and potential solutions.

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The Synergy of Federated Learning and IoT: Pioneering Privacy and Efficiency in Decentralised  Systems

  • 会议论文

  • 发表日期：

    2026年

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  • Achyuth Mukund,
  • T. Aditya Varun,
  • K. B. Sundharakumar

摘要

Federated Learning (FL) has emerged as a revolutionary machine learning paradigm that enables distributed model training without centralizing sensitive data, offering significant benefits for Internet of Things (IoT) applications. This paper explores the integration of FL within IoT environments, highlighting the challenges, recent advancements, and future directions. The study emphasizes privacy preservation, data heterogeneity, and communication efficiency while offering insights into emerging trends and potential solutions.