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As a framework for distributed online computing and model training, FL has shown significant potential for applications, e.g., IoT, autonomous driving, and remote medical care [24]. FL enables individual mobile clients to train a global model collectively without releasing their data [17]. In particular, each client trains its local model independently, relying on its local dataset, and sends the gradient of the local model to a server.

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Privacy-Aware Wireless Federated Learning

  • 章节

  • 发表日期：

    2026年

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  • Kai Li,
  • Xin Yuan,
  • Wei Ni

摘要

As a framework for distributed online computing and model training, FL has shown significant potential for applications, e.g., IoT, autonomous driving, and remote medical care [24]. FL enables individual mobile clients to train a global model collectively without releasing their data [17]. In particular, each client trains its local model independently, relying on its local dataset, and sends the gradient of the local model to a server.