  The growing concern over malicious attacks targeting the robustness of both
Centralized and Decentralized Federated Learning (FL) necessitates novel
defensive strategies. In contrast to the centralized approach, Decentralized FL
(DFL) has the advantage of utilizing network topology and local dataset
information, enabling the exploration of Moving Target Defense (MTD) based
approaches.
  This work presents a theoretical analysis of the influence of network
topology on the robustness of DFL models. Drawing inspiration from these
findings, a three-stage MTD-based aggregation protocol, called Voyager, is
proposed to improve the robustness of DFL models against poisoning attacks by
manipulating network topology connectivity. Voyager has three main components:
an anomaly detector, a network topology explorer, and a connection deployer.
When an abnormal model is detected in the network, the topology explorer
responds strategically by forming connections with more trustworthy
participants to secure the model. Experimental evaluations show that Voyager
effectively mitigates various poisoning attacks without imposing significant
resource and computational burdens on participants. These findings highlight
the proposed reactive MTD as a potent defense mechanism in the context of DFL.
