In the fast-progressing field of wireless communications, the forthcoming 6G networks are expected to revolutionize the way we communicate, offering unparalleled speed, minimal latency, and seamless connectivity. However, amid this evolution, the paramount concern remains the security and privacy of the data traversing these networks. Traditional centralized artificial intelligence (AI) techniques already struggle to keep up with the vast amount of data of future 6G networks and deal with the increasing worries about privacy. Federated learning (FL), emerges as a key enabler of Trustworthy AI (TAI), empowering the engagement of distributed network nodes in AI training without the need for exchanging raw data, thereby mitigating the risks associated with centralized data processing. In this paper, we provide a comprehensive survey on the potential of FL in enhancing the security of 6G networks. Particularly, we begin by providing the necessary background on 5G networks and FL, setting the stage for understanding their current and future implications. We then explore the current state-of-the-art of FL applications within 5G networks and their relevance to the future threat landscape of 6G. Subsequently, we examine the inherent vulnerabilities of FL systems, major attacks against FL in the context of 5G networks, and corresponding defense mechanisms. Finally, we discuss the integration of advanced FL technologies and concepts towards enhanced cybersecurity and privacy in 6G networks, aiming to cover all aspects and future perspectives of FL within the context of the forthcoming 6G threat landscape.

Fifth generation (5G) networks have significantly enhanced connectivity, speed, and reliability, transforming industries with faster and more efficient communication. The Internet of Things (IoT) has introduced unprecedented convenience and automation, revolutionizing sectors such as healthcare, finance, and smart infrastructure. However, both 5G networks and IoT environments are experiencing a high frequency of attacks. Intrusion detection systems (IDSs) built on federated learning (FL) are being proposed to boost data privacy and security. However, these IDSs are related with the inherent drawbacks of FL, namely the existence of non-independently and identically (non-IID) distributed features and the machine learning model complexity. To address these limitations, we present a study that integrates a Mixture of Experts (MoE) into an FL setting in the task of intrusion detection. Specifically, to mitigate the issues of model complexity within the FL setting, we use a sparsely gated MoE layer consisting of a router/gating network and a set of experts. Only a subset of experts is selected via applying noisy top-k gating. To alleviate the issue of non-IID data, we adopt the Label-based Dirichlet Partition method, utilizing Dirichlet sampling with a hyperparameter $\alpha$ to simulate a label-based non-IID data distribution. Four FL strategies are employed. We perform our experiments on the 5G-NIDD and BoT-IoT datasets. Findings show that the proposed approach achieves competitive performance across both datasets under heterogeneous federated settings.