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FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluders. (arXiv:2211.13389v1 [cs.CR])
Nov. 28, 2022, 2:10 a.m. | Hanlin Gu, Lixin Fan, Xingxing Tang, Qiang Yang
cs.CR updates on arXiv.org arxiv.org
This paper proposes a general spectral analysis framework that thwarts a
security risk in federated Learning caused by groups of malicious Byzantine
attackers or colluders, who conspire to upload vicious model updates to
severely debase global model performances. The proposed framework delineates
the strong consistency and temporal coherence between Byzantine colluders'
model updates from a spectral analysis lens, and, formulates the detection of
Byzantine misbehaviours as a community detection problem in weighted graphs.
The modified normalized graph cut is then …
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