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Radio access network (RAN) slicing is a key element in enabling current 5Gnetworks and next-generation networks to meet the requirements of differentservices in various verticals. However, the heterogeneous nature of theseservices' requirements, along with the limited RAN resources, makes the RANslicing very complex. Indeed, the challenge that mobile virtual networkoperators (MVNOs) face is to rapidly adapt their RAN slicing strategies to thefrequent changes of the environment constraints and service requirements.Machine learning techniques, such as deep reinforcement learning (DRL), areincreasingly considered a key enabler for automating the management andorchestration of RAN slicing operations. Nerveless, the ability to generalizeDRL models to multiple RAN slicing environments may be limited, due to theirstrong dependence on the environment data on which they are trained. Federatedlearning enables MVNOs to leverage more diverse training inputs for DRL withoutthe high cost of collecting this data from different RANs. In this paper, wepropose a federated deep reinforcement learning approach for RAN slicing. Inthis approach, MVNOs collaborate to improve the performance of their DRL-basedRAN slicing models. Each MVNO trains a DRL model and sends it for aggregation.The aggregated model is then sent back to each MVNO for immediate use andfurther training. The simulation results show the effectiveness of the proposedDRL approach.

Federated Deep Reinforcement Learning for Open RAN Slicing in 6G Networks