FeDistSlice: Federated Policy Distillation for Collaborative Intelligence in Multi-Tenant RAN Slicing

Federated Deep Reinforcement Learning (FDRL) for Radio Access Network (RAN) Slicing offers a promising approach for optimizing resource allocation and network performance, while also preserving data privacy for multiple tenants. However, the inherently non-independent and identically distributed (non-IID) nature of data, stemming from the diverse services and unique characteristics of RAN slices, poses significant challenges. This heterogeneity can disrupt the standard assumptions FDRL makes, leading to model training inefficiencies and potentially suboptimal slicing decisions. Addressing this non-IID challenge is imperative to harness the full potential of FDRL in RAN slicing and to ensure seamless, adaptive, and efficient resource sharing among the tenants. Hence, we propose FeDistSlice, a federated distillation slicing framework wherein multiple decision agents collaborate in real time, optimizing resource allocation tailored to each tenant's specific characteristics. Motivated by collaborative intelligence, we introduced a customized mutual policy distillation (MPD) strategy to foster collaboration across multiple tenants. This innovation allows for the creating of personalized models tailored to each agent's unique requirements and context. Through MPD, these models can collaboratively learn and refine their policies by leveraging insights from other agents within the network. Simulation results show that FeDistSlice converges more effectively and achieves increased robustness to non-IID data.