Multi-Layer Collaborative Federated Learning architecture for 6G Open RAN

Abstract

The emerging sixth-generation (6G) systems aim to integrate machine learning (ML) capabilities into the network architecture. Open Radio Access Network (O-RAN) is a paradigm that supports this vision. However, deep integration of 6G edge intelligence and O-RAN can face challenges in efficient execution of ML tasks due to finite link bandwidth and data privacy concerns. We propose a new Multi-Layer Collaborative Federated Learning (MLCFL) architecture for O-RAN, as well as a workflow and deployment design, which are demonstrated through the important RAN use case of intelligent mobility management. Simulation results show that MLCFL effectively improves the mobility prediction and reduces energy consumption and delay through flexible deployment adjustments. MLCFL has the potential to advance the O-RAN architecture design and provides guidelines for efficient deployment of edge intelligence in 6G.