Biasing Federated Learning Based on Adversarial Graph Attention Networks

Kai Li (), Xin Yuan () and Wei Ni ()
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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

Chapter 5 in Security and Resilience in Distributed Machine Learning, 2026, pp 53-79 from Springer

Abstract: 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].

Date: 2026
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DOI: 10.1007/978-3-032-23959-4_5

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