Qi Jiang

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

Experimental results demonstrate that the proposed framework improves convergence, accuracy, scalability, and signal-to-noise ratio, data rate, while simultaneously reducing latency, and energy consumption for both CIFAR-10 and FEMNIST datasets compared with the FedProx, FedADMM, LyFeD and FL-MEC benchmark schemes.