  Most machine learning applications rely on centralized learning processes,
opening up the risk of exposure of their training datasets. While federated
learning (FL) mitigates to some extent these privacy risks, it relies on a
trusted aggregation server for training a shared global model. Recently, new
distributed learning architectures based on Peer-to-Peer Federated Learning
(P2PFL) offer advantages in terms of both privacy and reliability. Still, their
resilience to poisoning attacks during training has not been investigated. In
this paper, we propose new backdoor attacks for P2PFL that leverage structural
graph properties to select the malicious nodes, and achieve high attack
success, while remaining stealthy. We evaluate our attacks under various
realistic conditions, including multiple graph topologies, limited adversarial
visibility of the network, and clients with non-IID data. Finally, we show the
limitations of existing defenses adapted from FL and design a new defense that
successfully mitigates the backdoor attacks, without an impact on model
accuracy.
