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The unprecedented rise in the growth of Internet of Things has created an unprecedentedly enormous network of connected devices exchanging sensitive data. This huge transmission of security sensitive data poses several security issues which can be seldom addressed by conventional machine learning methodologies, opening an avenue for decentralized learning. Here, Federated learning emerges as a problem solver by letting several devices train models at different clients eradicating the requirement to move raw data from IoT devices to a centralized server, addressing security concern. This study examines the viabilities of federated learning within Internet of Things, suggesting a federated client-server architecture explicitly designed for capacity-constrained IoT devices. Current research work presents different aggregation techniques, such as FedAdam, FedAvg, and FedAdagrad and their efficiencies in IoT environment. Experiments show that Federated learning yields results at par with centralized models while achieving cost effectiveness and data privacy. The obtained results demonstrate that federated learning has the potential to escalate the efficacy of IoT applications encompassing healthcare, industrial automation, and intelligent transportation systems.

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Federated Learning for IoT: A Privacy-Preserving Approach to Intelligent Edge Systems

  • 会议论文

  • 发表日期：

    2026年

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  • Nonita Sharma,
  • Monika Mangla,
  • Ravi Sharma,
  • Manik Rakhra

摘要

The unprecedented rise in the growth of Internet of Things has created an unprecedentedly enormous network of connected devices exchanging sensitive data. This huge transmission of security sensitive data poses several security issues which can be seldom addressed by conventional machine learning methodologies, opening an avenue for decentralized learning. Here, Federated learning emerges as a problem solver by letting several devices train models at different clients eradicating the requirement to move raw data from IoT devices to a centralized server, addressing security concern. This study examines the viabilities of federated learning within Internet of Things, suggesting a federated client-server architecture explicitly designed for capacity-constrained IoT devices. Current research work presents different aggregation techniques, such as FedAdam, FedAvg, and FedAdagrad and their efficiencies in IoT environment. Experiments show that Federated learning yields results at par with centralized models while achieving cost effectiveness and data privacy. The obtained results demonstrate that federated learning has the potential to escalate the efficacy of IoT applications encompassing healthcare, industrial automation, and intelligent transportation systems.