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.
Kai Qiao, Hongchao Wang, Zi-Hao Zhang et al.· IEEE Transactions on Mobile...· 0 citations
Deterministic transmission and computation are essential for open radio access networks (O-RANs) to support latency-critical and computation-intensive applications. However, existing O-RAN and time-sensitive networking integrations mainly focus on deterministic guarantees in wired fronthaul transmission, while lacking a unified mechanism to coordinate wireless transmission, wired transmission, and computation. This limitation makes it difficult to provide bounded end-to-end latency under time-varying wireless channels and increasing computational demands, which reduces flow scheduling success rates and causes resource wastage. In this paper, we propose a hierarchical deterministic O-RAN framework, named DetO-RAN, which ensures deterministic transmission and computation for flows through a unified queuing and resource allocation mechanism. DetO-RAN introduces a three-queue model (i.e., wireless, wired, and computing queues) to support transmission and computation of flows, and establishes a closed-loop resource allocation process based on model training, inference, and updating. Based on this framework, we formulate a multi-objective optimization problem aiming to maximize the flow scheduling success rate and resource utilization. Due to the time-varying wireless channels, strongly coupled resources, and network dynamics, traditional optimization algorithms are inefficient. Thus, we decouple the problem into a radio resource block (RB) allocation subproblem as well as a time slot and computing resource allocation subproblem. Furthermore, we propose a meta-learning-based dual-timescale resource allocation (ML-DTRA) algorithm to solve them. Specifically, ML-DTRA performs RB allocation via a graph neural network at a small timescale and makes time slot and computing resource allocation decisions via deep reinforcement learning at a large timescale. Extensive simulation results demonstrate that the ML-DTRA algorithm significantly improves the scheduling success rate by 12.7% and the resource utilization by 15.9% compared with state-of-the-art benchmarks, showing its effectiveness in supporting end-to-end deterministic transmission and computation while improving resource efficiency in dynamic O-RAN environments.
Kai Qiao, Hao Xiong, Qin-Ding Wang et al.· IEEE Transactions on Mobile...· 1 citation
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