Communication-Efficient Disaggregated and Distributed Federated Learning in NG-RANs

Next Generation Radio Access Networks (NG-RANs) are a promising paradigm for meeting 6G and future application requirements. However, the practical implementation of NG-RAN systems faces significant challenges due to novel technologies, network densification, and more complex applications. Specifically, the limited capacity of front-haul links and privacy concerns have posed severe constraints that must be addressed. To overcome these obstacles, we present a novel approach, called FedBNG, which is a disaggregated and distributed Federated Learning (FL)-based algorithm for NG-RAN. This algorithm enables collaboration between User Equipment (UEs) and the NG-RAN infrastructure through a learning process and shared prediction models, ultimately improving privacy and alleviating the burden on the front-haul interface. Using a shared predictive model, our proposed approach facilitates cooperative learning between Radio Units (RUs) and Distributed Units (DUs). To accomplish this, we initially used the first-phase training models of RUs and DUs as input for local training. Subsequently, the suboptimal DU models are uploaded to the Central Unit (CU) for the next phase of global training. We present numerical results to evaluate the efficacy of our proposed approach in terms of accuracy, service latency, and traffic volume. Our algorithm’s convergence properties demonstrate that it outperforms the current state-of-the-art solution based on FedAvg.