Mitigating Communications Threats in Decentralized Federated Learning through Moving Target Defense. (arXiv:2307.11730v1 [cs.CR])

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 …

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