DEEPFL: A Differential Evolution-Based Framework for Privacy Protection and Poisoning Attack Defense in Maritime Edge Computing

Abstract

Federated learning (FL) is crucial in edge computing for next-generation wireless networks because it enables collaborative learning among devices while protecting data privacy. However, marine edge networks encounter complex issues, including frequent network disruptions, highly dynamic network topologies, and stringent bandwidth limitations, compared to traditional FL environments. Additionally, the heterogeneity of devices and unconventional data distribution complicate model training further. Moreover, due to the broadcast nature and dynamic vulnerability of wireless medium, edge nodes are susceptible to interference or malicious attacks, such as information theft and poisoning attacks, leading to inaccurate or even failed learning outcomes. Many distributed learning algorithms assume stable node environments, overlooking security and communication bottlenecks, which reduces learning efficiency and model performance. Therefore, researching privacy protection and defenses against poisoning attacks in federated learning within edge computing networks is of significant importance and value. To address this, we propose the Differential Evolution Edge-Partially Federated Learning framework, which aims to secure gradient communication by leveraging the characteristics of heterogeneous devices and non-independent and identically distributed (Non-IID) data, thereby reducing the risk of information leakage during gradient communication and effectively mitigating the impact of data heterogeneity in edge environments. Additionally, we have designed a shared-layer mean detection method based on this framework to detect and defend against poisoning attacks. This algorithm protects the security of gradient information without significantly increasing computational and communication overhead. Experimental results demonstrate that, in marine edge computing environments, this framework improves privacy protection performance by 20% and increases poisoning attack defense rates by 10% without adding extra computational costs.