Lyapunov-DLD-Based Latency and Power Optimization in 5G O-RAN for Federated Learning

Abstract

Recent Federated Learning (FL)-enabled 5G Open Radio Access Network (O-RAN) systems continue to face significant challenges associated with scalability, convergence speed, dynamic power allocation, and the adaptive optimization of model weights. Furthermore, the stochastic nature of wireless channels and increasing user density often degrade latency performance and reliable model aggregation during federated communication rounds. To address these challenges, this paper proposes a hybrid Lyapunov–DLD framework that integrates Lyapunov Drift-Plus-Penalty (LDPP) control with Dual Lagrange Decomposition (DLD) to enable self-organizing network (SON)-driven adaptive Open Radio Unit (O-RU) resource allocation, thereby ensuring timely model updates between users and the server. Specifically, the LDPP mechanism dynamically provisions O-RU computational and bandwidth resources according to real-time environmental drift, while Projected Momentum Gradient Descent (PMGD) accelerates convergence and improves penalty adaptation efficiency. Leveraging the bandwidth allocated by LDPP, the DLD framework subsequently performs channel-aware transmission power allocation based on Channel State Information (CSI) to ensure reliable model weight delivery under stochastic radio conditions. In addition, the dynamic model weight generation problem at the user side is addressed using a Proximal Policy Optimization (PPO)-based framework integrated with the Douglas–Rachford Federated Learning (FeDR) model, which mitigates the impact of non-IID data distributions arising from heterogeneous user datasets and accelerates the convergence of the local loss function through adaptive local model parameter optimization. Experimental results demonstrate that the proposed framework improves convergence, accuracy, scalability, and signal-to-noise ratio (SNR), data rate, while simultaneously reducing latency, and energy consumption for both CIFAR-10 and FEMNIST datasets compared with the FedProx, FedADMM, LyFeD (Lyapunov-based federated learning) and FL-MEC benchmark schemes.