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Federated learning (FL) is well-suited to 5G networks, where many mobile devices generate sensitive edge data. Secure aggregation protocols enhance privacy in FL by ensuring that individual user updates reveal no information about the underlying client data. However, the dynamic and large-scale nature of 5G-marked by high mobility and frequent dropouts-poses significant challenges to the effective adoption of these protocols. Existing protocols often require multi-round communication or rely on fixed infrastructure, limiting their practicality. We propose a lightweight, single-round secure aggregation protocol designed for 5G environments. By leveraging base stations for assisted computation and incorporating precomputation, key-homomorphic pseudorandom functions, and t-out-of-k secret sharing, our protocol ensures efficiency, robustness, and privacy. Experiments show strong security guarantees and significant gains in communication and computation efficiency, making the approach well-suited for real-world 5G FL deployments. 

Networks are today a critical infrastructure. Their resilience against attacks is thus crucial. Protecting networks requires a comprehensive security life-cycle and the deployment of different protection techniques. To make defenses more effective, recent solutions leverage AI techniques. In this paper, we discuss AI-based protection techniques, according to a security life-cycle consisting of several phases: (i) Prepare; (ii) Monitor and Diagnose; and (iii) React, Recovery and Fix. For each phase, we discuss relevant AI techniques, initial approaches, and research directions.