HFedAR: Resource Allocation for Adaptive Retransmission-Based Hierarchical Federated Learning in Open RAN

Abstract

The flexible architecture of the open radio access network (O-RAN) provides effective support for the deployment of federated learning (FL). However, existing FL schemes in wireless networks often suffer from packet errors, which lead to reduced model test accuracy and increased training delay. In this paper, we propose a robust FL scheme in O-RAN, named adaptive retransmission-based hierarchical FL (HFedAR), which improves model test accuracy and reduces training delay. Specifically, HFedAR first analyzes the similarity of local gradients using a clustering algorithm to associate users with edge servers. Subsequently, we employ an adaptive retransmission mechanism for both edge and global aggregation, thereby facilitating rapid convergence of FL on non-independent and identically distributed data. Considering the limitations of spectrum resources and user energy, we formulate a multi-objective optimization problem to minimize FL training loss and delay. Due to the implicit nature of the objective function, the coupling of decision variables, and network dynamics, it is difficult to solve the problem through traditional convex optimization and machine learning algorithms. Therefore, we derive a convergence upper bound for HFedAR and present a two-stage resource allocation algorithm. The algorithm can jointly make optimal user power and computing resource allocation decisions in the first stage, and user scheduling, retransmission selection, and radio resource block allocation decisions in the second stage. Extensive simulation results on the MNIST and CIFAR-10 datasets demonstrate that the HFedAR scheme significantly improves model test accuracy by 3.5% and 6.5% and reduces training delay by 40.1% and 34.2% compared with existing FL benchmarks.