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Under the FL framework, each user independently trains its local model utilizing proprietary data, subsequently generating ML model updates that are transmitted to a server without revealing the user’s confidential data [45]. The server, in turn, amalgamates these model updates, to create a global model, which is then disseminated back to the users to instigate the ensuing round of FL training [19]. Inherent in the FL methodology is the safeguarding of individual data privacy, achieved through obviating the necessity to share private data [10].

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Biasing Federated Learning Based on Adversarial Graph Attention Networks

  • 章节

  • 发表日期：

    2026年

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  • Kai Li,
  • Xin Yuan,
  • Wei Ni

摘要

Under the FL framework, each user independently trains its local model utilizing proprietary data, subsequently generating ML model updates that are transmitted to a server without revealing the user’s confidential data [45]. The server, in turn, amalgamates these model updates, to create a global model, which is then disseminated back to the users to instigate the ensuing round of FL training [19]. Inherent in the FL methodology is the safeguarding of individual data privacy, achieved through obviating the necessity to share private data [10].