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, highlighting the potential risks of DFL communications. In response to these identified risks, this work introduces a security module designed for DFL platforms to counter communication-based attacks. The module combines security techniques such as symmetric and asymmetric encryption with Moving Target Defense (MTD) techniques, including random neighbor selection and IP/port switching. The security module is implemented in a DFL platform called Fedstellar, allowing the deployment and monitoring of the federation. A DFL scenario has been deployed, involving eight physical devices implementing three security configurations: (i) a baseline with no security, (ii) an encrypted configuration, and (iii) a configuration integrating both encryption and MTD techniques. The effectiveness of the security module is validated through experiments with the MNIST dataset and eclipse attacks. The results indicated an average F1 score of 95%, with moderate increases in CPU usage (up to 63.2% +-3.5%) and network traffic (230 MB +-15 MB) under the most secure configuration, mitigating the risks posed by eavesdropping or eclipse attacks.

这篇论文通过揭示分散式联邦学习（DFL）通信潜在风险，介绍一种专为DFL平台设计的安全模块，结合对称和非对称加密以及移动目标防御（MTD）技术，有效地应对通信攻击，并通过实验证实了该安全模块的有效性。

通过移动目标防御减轻分散联合学习中的通信威胁