pFedDef: Defending Grey-Box Attacks for Personalized Federated Learning. (arXiv:2209.08412v1 [cs.LG])

Personalized federated learning allows for clients in a distributed system to
train a neural network tailored to their unique local data while leveraging
information at other clients. However, clients' models are vulnerable to
attacks during both the training and testing phases. In this paper we address
the issue of adversarial clients crafting evasion attacks at test time to
deceive other clients. For example, adversaries may aim to deceive spam filters
and recommendation systems trained with personalized federated learning for
monetary …

attacks box federated learning