Secure and Privacy-Preserving Federated Learning-Based Resource Allocation for Next Generation Networks

This paper surveys recent research on Federated Learning-based resource allocation for next-generation networks with the aim of identifying research gaps and potential directions for future work. We start by outlining the main challenges and requirements for secure and privacy-preserving resource allocation in these networks. The existing solutions and techniques proposed in literature are then reviewed. The strengths and limitations of these solutions are analyzed and trade-offs in terms of security, privacy, efficiency, and scalability are discussed. The research gaps and promising directions for future research are identified, such as integrating multiple solutions (e.g., Game theory or optimization techniques with Federated Learning) and developing new models and algorithms for AI-based resource allocation that addresses specific challenges and requirements of next-generation networks. The goal is to inspire further research in this critical and rapidly evolving field.