Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective

arXiv:2309.16456v2 Announce Type: replace
Abstract: Existing approaches defend against backdoor attacks in federated learning (FL) mainly through a) mitigating the impact of infected models, or b) excluding infected models. The former negatively impacts model accuracy, while the latter usually relies on globally clear boundaries between benign and infected model updates. However, model updates are easy to be mixed and scattered throughout in reality due to the diverse distributions of local data. This work focuses on excluding infected models in FL. …

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