In emerging networked systems, mobile edge devices such as ground vehicles and unmanned aerial system (UAS) swarms collectively aggregate vast amounts of data to make machine learning decisions such as threat detection in remote, dynamic, and infrastructure-constrained environments where power and bandwidth are scarce. Federated learning (FL) addresses these constraints and privacy concerns by enabling nodes to share local model weights for deep neural networks instead of raw data, facilitating more reliable decision-making than individual learning. However, conventional FL relies on a central server to coordinate model updates in each learning round, which imposes significant computational burdens on the central node and may not be feasible due to the connectivity constraints. By eliminating dependence on a central server, distributed federated learning (DFL) offers scalability, resilience to node failures, learning robustness, and more effective defense strategies. Despite these advantages, DFL remains vulnerable to increasingly advanced and stealthy cyberattacks. In this paper, we design sophisticated targeted training data poisoning and backdoor (Trojan) attacks, and characterize the emerging vulnerabilities in a vehicular network. We analyze how DFL provides resilience against such attacks compared to individual learning and present effective defense mechanisms to further strengthen DFL against the emerging cyber threats.

翻译：在新兴网络化系统中，移动边缘设备（如地面车辆和无人系统集群）在电力与带宽资源稀缺的远程、动态且基础设施受限的环境中，通过协同聚合海量数据实现机器学习决策（例如威胁检测）。联邦学习通过使节点共享深度神经网络的本地模型权重而非原始数据，解决了这些约束与隐私问题，从而实现了比个体学习更可靠的决策。然而，传统联邦学习依赖中央服务器协调每轮学习中的模型更新，这给中心节点带来巨大计算负担，且可能因连接限制而无法实现。分布式联邦学习通过消除对中央服务器的依赖，提供了可扩展性、节点故障恢复能力、学习鲁棒性及更有效的防御策略。尽管存在这些优势，分布式联邦学习仍面临日益复杂和隐蔽的网络攻击威胁。本文设计了精密的针对性训练数据投毒与后门（木马）攻击，并刻画了车载网络中新兴的安全漏洞。我们分析了相较于个体学习，分布式联邦学习如何对此类攻击具备恢复能力，并提出了有效的防御机制以进一步增强分布式联邦学习应对新兴网络威胁的能力。