  This paper explores previously unknown backdoor risks in HyperNet-based
personalized federated learning (HyperNetFL) through poisoning attacks. Based
upon that, we propose a novel model transferring attack (called HNTroj), i.e.,
the first of its kind, to transfer a local backdoor infected model to all
legitimate and personalized local models, which are generated by the HyperNetFL
model, through consistent and effective malicious local gradients computed
across all compromised clients in the whole training process. As a result,
HNTroj reduces the number of compromised clients needed to successfully launch
the attack without any observable signs of sudden shifts or degradation
regarding model utility on legitimate data samples making our attack stealthy.
To defend against HNTroj, we adapted several backdoor-resistant FL training
algorithms into HyperNetFL. An extensive experiment that is carried out using
several benchmark datasets shows that HNTroj significantly outperforms data
poisoning and model replacement attacks and bypasses robust training algorithms
even with modest numbers of compromised clients.

Machine Learning;Cryptography and Security