Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning
This work addresses resource optimization for federated learning in wireless networks, offering incremental improvements in scheduling efficiency.
The paper tackles the problem of maximizing model accuracy within a limited training time budget for latency-constrained wireless federated learning by proposing a joint device scheduling and resource allocation policy, which outperforms state-of-the-art scheduling policies in experiments across various data distributions and cell radii.
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 in each step selects the device consuming the least updating time obtained by the optimal bandwidth allocation, 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.