Abstract
With the rapid popularization of 5G edge communication systems, their decentralized architecture and the ultra-large attack surface brought by massive access devices have created severe network security challenges. Although current artificial intelligence-based intrusion detection systems and conventional federated learning schemes can achieve high intrusion detection accuracy, they universally suffer from two major limitations: excessive computational overhead and vulnerability to poisoning attacks, which make them unable to adapt to resource-constrained edge deployment environments. To address this issue, this paper proposes a lightweight anti-poisoning federated learning framework for low-latency secure 5G edge communications, with two core design elements: First, it integrates lightweight GRU/CNN models and a trust-aware aggregation mechanism to weaken the negative impact of malicious model updates; second, it introduces a deviation-based poisoning detection strategy to identify and filter adversarial updates during the federated learning process. We used the public CICIDS2017 dataset to complete tests in a simulated 5G edge environment equipped with 100 to 1000 edge devices, covering multiple scenarios including DDoS, MITM, and poisoning attacks. The results show that compared with existing similar schemes, the proposed framework reaches a maximum detection accuracy of 98.3\%, reduces communication latency by 30-35\%, cuts computational overhead by 40-60\%, and sees only a negligible drop in accuracy under poisoning attacks. It can support a wide range of real-time 5G edge applications such as intelligent transportation, smart cities, and the industrial Internet of Things.