Backdoor Attacks in Peer-to-Peer Federated Learning. (arXiv:2301.09732v1 [cs.LG])

We study backdoor attacks in peer-to-peer federated learning systems on
different graph topologies and datasets. We show that only 5% attacker nodes
are sufficient to perform a backdoor attack with 42% attack success without
decreasing the accuracy on clean data by more than 2%. We also demonstrate that
the attack can be amplified by the attacker crashing a small number of nodes.
We evaluate defenses proposed in the context of centralized federated learning
and show they are ineffective in peer-to-peer …

accuracy attack attacks backdoor backdoor attacks context data datasets federated learning nodes peer-to-peer study systems