  Federated learning (FL) enables multiple clients to collaboratively train
machine learning models without revealing their private training data. In
conventional FL, the system follows the server-assisted architecture
(server-assisted FL), where the training process is coordinated by a central
server. However, the server-assisted FL framework suffers from poor scalability
due to a communication bottleneck at the server, and trust dependency issues.
To address challenges, decentralized federated learning (DFL) architecture has
been proposed to allow clients to train models collaboratively in a serverless
and peer-to-peer manner. However, due to its fully decentralized nature, DFL is
highly vulnerable to poisoning attacks, where malicious clients could
manipulate the system by sending carefully-crafted local models to their
neighboring clients. To date, only a limited number of Byzantine-robust DFL
methods have been proposed, most of which are either communication-inefficient
or remain vulnerable to advanced poisoning attacks. In this paper, we propose a
new algorithm called BALANCE (Byzantine-robust averaging through local
similarity in decentralization) to defend against poisoning attacks in DFL. In
BALANCE, each client leverages its own local model as a similarity reference to
determine if the received model is malicious or benign. We establish the
theoretical convergence guarantee for BALANCE under poisoning attacks in both
strongly convex and non-convex settings. Furthermore, the convergence rate of
BALANCE under poisoning attacks matches those of the state-of-the-art
counterparts in Byzantine-free settings. Extensive experiments also demonstrate
that BALANCE outperforms existing DFL methods and effectively defends against
poisoning attacks.
