Device Scheduling and Resource Allocation for Federated Learning under Delay and Energy Constraints

Federated Learning (FL) is an emerging technique to enhance edge intelligence, where mobile devices train machine learning models collaboratively with their local data. Limited energy on devices and scarce wireless bandwidth can notably impact the convergence of FL over wireless networks, and thus device scheduling and resource allocation are critical. In this paper, we propose a joint device scheduling and resource allocation scheme to maximize the model accuracy under total training delay and device energy budgets. Since FL consists of multiple training rounds, there is an inherent trade-off between per-round delay, per-round energy consumption, and the total number of rounds. To find solution, we decouple the accuracy maximization problem into two sub-problems. First, given a scheduling policy, the bandwidth allocation and local computing frequency are jointly optimized to maximize the number of rounds that can be conducted. Then, a device scheduling policy is proposed to balance the trade-off between the per-round energy and delay cost and the number of rounds, with the ultimate goal of accuracy optimization. Experiments on various learning tasks and datasets show that the proposed scheme can greatly improve the convergence rate of resource-constrained FL.