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Biasing Federated Learning Based on Adversarial Graph Attention Networks

In: Security and Resilience in Distributed Machine Learning

Author

Listed:
  • Kai Li

    (University of Luxembourg, Interdisciplinary Centre for Security, Reliability and Trust (SnT))

  • Xin Yuan

    (Commonwealth Scientific and Industrial Research Organisation (CSIRO), Data61 Business Unit)

  • Wei Ni

    (Commonwealth Scientific and Industrial Research Organisation (CSIRO), Data61 Business Unit)

Abstract

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].

Suggested Citation

  • Kai Li & Xin Yuan & Wei Ni, 2026. "Biasing Federated Learning Based on Adversarial Graph Attention Networks," Springer Series in Reliability Engineering, in: Security and Resilience in Distributed Machine Learning, chapter 5, pages 53-79, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-032-23959-4_5
    DOI: 10.1007/978-3-032-23959-4_5
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