Energy-Efficient Online Federated Learning in Wireless Networks

In this work, we study energy-efficient online federated learning. First, we analyzed the convergence performance of device selection in dynamic and non-stationary conditions, deriving the upper bound of the convergence rate. Building on this, we formulated an optimization problem that minimizes energy consumption while ensuring convergence and meeting latency constraints. Second, we developed an online allocation and scheduling-based iterative strategy (OASIS). Here, we designed a combinatorial upper-confidence-bound-based device scheduling algorithm with a newly designed reward function. We also derived a Lambert function-based power allocation to the scheduled devices in closed form. We performed a dynamic regret analysis, which reveals that the proposed algorithm effectively adapts to dynamic environments and maintains near-optimal decisions over time. It also demonstrates that the proposed algorithm achieves sublinear regret in a slowly changing dynamic environment and optimal regret in a static environment. Experimental results show that the proposed OASIS achieves faster convergence and provides significantly lower energy consumption than existing conventional baseline strategies. We also confirmed that the performance gain increases as the target accuracy level becomes higher. These results validate the energy efficiency and robustness of the proposed approach in realistic and time-varying federated learning environments.