Computation and Communication Resource Optimization for Efficient Hierarchical Federated Learning

Federated Learning (FL) is a sophisticated decentralized approach in machine learning, where individual user devices perform model training locally and then transmit their parameters to a central server. This paper delves into the dynamics of a hierarchical FL system, particularly focusing on the intricate exchange of model parameters between cloud and edge servers, and between edge servers and user devices. The capacity of this hierarchical FL model to achieve predefined global model accuracy is significantly influenced by delays in communication and computation of model parameters. To address these challenges and balance the trade-off between energy consumption and execution time, we formulate a comprehensive joint computation and communication resource optimization problem. This problem is aimed at minimizing the weighted sum of FL completion time and total energy consumption, enhancing overall system efficiency. The problem is methodically decomposed into four sub-problems. The initial three sub-problems utilize fractional programming techniques to find efficient solutions that optimize resource allocation. For the final sub-problem, we employ the Concave-Convex Procedure (CCCP), which is well-suited for dealing with the non-linear aspects of the optimization challenge. We then discuss the computational complexity and give the convergence analysis of the proposed approach. Simulation results underline the effectiveness of our approach, showing faster convergence of the global model under optimized communication and computation conditions. These results clearly demonstrate the beneficial trade-off between FL completion time and energy consumption, indicating that our optimization strategy successfully enhances the performance in hierarchical federated learning systems.