Energy Efficiency Maximization in Wireless Federated Learning under Inter-Channel Interference

Federated Learning FL offers a collaborative learning paradigm for a large number of devices while avoiding data centralization, which is particularly advantageous in wireless environments. However, inter-channel interference, a factor not fully explored in existing FL studies, significantly impacts model transmission and poses substantial challenges for resource allocation in the Wireless FL WFL framework. Additionally, the limited energy budgets of mobile devices necessitate energy-efficient strategies across both local computation and model transmission phases. To address these challenges, we formulate a joint learning and communication optimization problem aimed at maximizing the system's Energy Efficiency EE under given constraints. We address the problem by decomposing it into two sub-problems: power allocation and client selection, then tackling them sequentially. First, a designed graph neural network GNN is employed to parameterize the power allocation strategy, which is optimized through a primal-dual algorithm. Based on the power allocation model, we propose an online algorithm for energy-efficient client selection. Experimental results demonstrate that the proposed method achieves superior EE and reduced energy consumption compared to three baseline methods, while ensuring high-quality wireless transmission and achieving comparable global model accuracy.