Federated Learning (FL) is an intriguing distributed machine learning approach due to its privacy-preserving characteristics. To balance the trade-off between energy and execution latency, and thus accommodate different demands and application scenarios, we formulate an optimization problem to minimize a weighted sum of total energy consumption and completion time through two weight parameters. The optimization variables include bandwidth, transmission power and CPU frequency of each device in the FL system, where all devices are linked to a base station and train a global model collaboratively. Through decomposing the non-convex optimization problem into two subproblems, we devise a resource allocation algorithm to determine the bandwidth allocation, transmission power, and CPU frequency for each participating device. We further present the convergence analysis and computational complexity of the proposed algorithm. Numerical results show that our proposed algorithm not only has better performance at different weight parameters (i.e., different demands) but also outperforms the state of the art.

翻译：联邦学习（FL）因其隐私保护特性而成为一种引人关注的分布式机器学习方法。为了平衡能耗与执行延迟之间的权衡，从而适应不同的需求和应用场景，我们构建了一个优化问题，通过两个权重参数最小化总能耗与完成时间的加权和。优化的变量包括联邦学习系统中每个设备的带宽、传输功率和CPU频率，其中所有设备均连接至一个基站，并协同训练一个全局模型。通过将非凸优化问题分解为两个子问题，我们设计了一种资源分配算法，用以确定每个参与设备的带宽分配、传输功率和CPU频率。我们进一步给出了所提算法的收敛性分析和计算复杂度。数值结果表明，我们的算法不仅在不同权重参数（即不同需求）下具有更优性能，而且优于现有最先进方法。