Abstract

5G network slicing offers the potential to tailor network resources to diverse service requirements, but efficient and personalized resource allocation remains a significant challenge. Traditional centralized approaches struggle with scalability, privacy concerns, and the dynamic nature of user demands. This paper proposes a novel Federated Deep Reinforcement Learning (FDRL) framework for personalized resource allocation in 5G network slicing. The framework leverages federated learning to train a global deep reinforcement learning agent collaboratively across multiple edge servers, without sharing raw user data. Each edge server acts as a local agent, learning optimal resource allocation policies based on its local user data and contributing to the global model update. The proposed FDRL framework is designed to address the limitations of centralized approaches by enabling personalized resource allocation while preserving user privacy and enhancing scalability. We evaluate the performance of the FDRL framework through extensive simulations, demonstrating its superiority over centralized and non-federated DRL approaches in terms of resource utilization, service satisfaction, and privacy preservation. Furthermore, we analyze the impact of key parameters, such as the number of federated clients and the degree of data heterogeneity, on the performance of the FDRL framework.