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Billions of phones and IoT devices are generating a huge amount of data every second. These data provide insightful information to improve products via developing smarter AI models. However, the data generated by the users is always privacy-sensitive. In this section, we introduce a distributed learning technique called federated learning that enables a number of clients to train a shared global model collaboratively without transferring their local data.

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Federated Learning

  • 章节

  • 发表日期：

    2026年

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  • Yiran Chen,
  • Hai Li,
  • Huanrui Yang

摘要

Billions of phones and IoT devices are generating a huge amount of data every second. These data provide insightful information to improve products via developing smarter AI models. However, the data generated by the users is always privacy-sensitive. In this section, we introduce a distributed learning technique called federated learning that enables a number of clients to train a shared global model collaboratively without transferring their local data.