A Hierarchical Federated Learning Paradigm in O-RAN for Resource-Constrained IoT Devices

In the sixth generation (6G) networks, empowered by artificial intelligence/machine learning (AI/ML) techniques, the Internet of Things (IoT) is of paramount importance to realize ubiquitous connectivity for massive devices. As an open and flexible architecture, Open Radio Access Network (O-RAN) has enabled the integration of a variety of AI/ML paradigms, including federated learning. However, due to the heterogeneity of IoT devices, the generalization capability of federated learning models and energy efficiency in O-RAN architecture cannot be well guaranteed. In order to tackle these challenges, a hierarchical federated learning paradigm is proposed for resource-constrained IoT devices. First, a client-edge-cloud collaboration framework in O-RAN is designed, in which the cell-level federated learning models generated on the edge are integrated on the regional cloud based on ensemble learning method for adapting to diverse inference data streams of IoT devices. Second, the generalization performance of the framework is analyzed in terms of inference accuracy gap. A tractable upper bound can be obtained, based on which an algorithm is designed to minimize the gap by jointly optimizing on device scheduling, resource allocation and power control until approaching an optimal solution. Finally, the simulation results are provided to verify the performance gains in enhancing inference accuracy and reducing power consumption.