Dynamic Federated Learning over Open Radio Access Networks: Addressing Multi-Granular System Dynamics through Dedicated MAC Schedulers and Asymmetric User Selection

This paper introduces a novel dynamic federated learning system orchestrator, called DCLM, that leverages the flexibility of open radio access networks (O-RAN) to address multi-granular system dynamics, including time-varying wireless channel capacities and dynamic user datasets. DCLM employs dedicated MAC schedulers, hierarchical device-to-device assisted model training, and asymmetric user selection to optimize model performance, training latency, and energy consumption under dynamic conditions.