A Clustered Federated Learning Paradigm with Model Ensemble in O-RAN

The open radio access network (O-RAN) is evolving towards open and inter-operable RAN architecture by introducing the RAN Intelligent Controller (RIC) for the sixth generation (6G) communication systems. The integration of artificial intelligence/machine learning (AI/ML) models into O-RAN is critical to enabling network intelligence, in which federated learning has been proposed as an efficient paradigm. However, the generalization capability of federated learning models cannot be well guaranteed under heterogenous wireless circumstances. Therefore, to improve the generalization performance of models in O-RAN and to ensure the near real-time intelligence, in this paper, we have proposed a paradigm of clustered federated learning with model ensemble. First, the framework is provided, in which clustered federated learning is implemented for model training to improve convergence speed, and transfer learning is executed for near real-time model inference to adapt to diverse target tasks. Second, the generalization performance compared with optimal learning scheme is analyzed and an upper bound can be derived, based on which a joint optimization algorithm is sophisticatedly designed on transmit power control, user clustering and model ensemble. Finally, the proposed scheme is evaluated on the MNIST and CIFAR-10 data sets, which verifies the significant performance gains for deploying federated learning in O-RAN.