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On the Vulnerability of Backdoor Defenses for Federated Learning. (arXiv:2301.08170v1 [cs.LG])
cs.CR updates on arXiv.org arxiv.org
Federated Learning (FL) is a popular distributed machine learning paradigm
that enables jointly training a global model without sharing clients' data.
However, its repetitive server-client communication gives room for backdoor
attacks with aim to mislead the global model into a targeted misprediction when
a specific trigger pattern is presented. In response to such backdoor threats
on federated learning, various defense measures have been proposed. In this
paper, we study whether the current defense mechanisms truly neutralize the
backdoor threats from …
aim attacks backdoor backdoor attacks client clients communication data defense distributed federated learning global machine machine learning paradigm popular response server sharing study threats training trigger vulnerability