In federated learning (FL), devices contribute to the global training by uploading their local model updates via wireless channels. Due to limited computation and communication resources, device scheduling is crucial to the convergence rate of FL. In this paper, we propose a joint device scheduling and resource allocation policy to maximize the model accuracy within a given total training time budget for latency constrained wireless FL. A lower bound on the reciprocal of the training performance loss, in terms of the number of training rounds and the number of scheduled devices per round, is derived. Based on the bound, the accuracy maximization problem is solved by decoupling it into two sub-problems. First, given the scheduled devices, the optimal bandwidth allocation suggests allocating more bandwidth to the devices with worse channel conditions or weaker computation capabilities. Then, a greedy device scheduling algorithm is introduced, which selects the device consuming the least updating time obtained by the optimal bandwidth allocation in each step, until the lower bound begins to increase, meaning that scheduling more devices will degrade the model accuracy. Experiments show that the proposed policy outperforms state-of-the-art scheduling policies under extensive settings of data distributions and cell radius.

中文翻译：

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延迟受限无线联邦学习的联合设备调度和资源分配


在联邦学习（FL）中，设备通过无线通道上传本地模型更新来促进全局训练。由于计算和通信资源有限，设备调度对于FL的收敛速度至关重要。在本文中，我们提出了一种联合设备调度和资源分配策略，以在给定的总训练时间预算内最大化延迟受限无线 FL 的模型精度。根据训练轮数和每轮调度设备的数量得出训练性能损失倒数的下限。基于界限，精度最大化问题通过将其解耦为两个子问题来解决。首先，给定调度的设备，最佳带宽分配建议向信道条件较差或计算能力较弱的设备分配更多带宽。然后，引入贪婪设备调度算法，该算法在每一步中选择通过最优带宽分配获得的更新时间消耗最少的设备，直到下界开始增加，这意味着调度更多设备会降低模型精度。实验表明，在数据分布和小区半径的广泛设置下，所提出的策略优于最先进的调度策略。