Mitigating Communications Threats in Decentralized Federated Learning through Moving Target Defense

Understand

The rise of Decentralized Federated Learning (DFL) has enabled the training of machine learning models across federated participants, fostering decentralized model aggregation and reducing dependence on a server.

• However, this approach introduces unique communication security challenges that have yet to be thoroughly addressed in the literature.
• These challenges primarily originate from the decentralized nature of the aggregation process, the varied roles and responsibilities of the participants, and the absence of a central authority to oversee and mitigate threats.
• Addressing these challenges, this paper first delineates a comprehensive threat model focused on DFL communications.