Efficient Federated Learning in Resource-Constrained Edge Intelligence Networks Using Model Compression
Chao Chen, Bohang Jiang, Shengli Liu, Chuanhuang Li, Celimuge Wu, Rui Yin
Abstract
We investigate energy-efficient <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">federated learning</i> (FL) in computation and communication resource-constrained edge intelligence networks using model compression. An edge device selection strategy is designed to select appropriate edge devices for participating in FL at the beginning of each training iteration. An optimization problem is then formulated to jointly optimize the pruning ratio, CPU frequency, uplink power, and bandwidth allocation for the selected edge devices. Due to the non-convexity of the optimization problem, it is decomposed into three subproblems, and closed-form solutions or efficient algorithms are developed for each subproblem. Based on these solutions, an alternating optimization algorithm is constructed to solve the original problem. Simulation results show that the proposed scheme outperforms baseline schemes in improving the energy efficiency of FL.