Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning
This work addresses efficiency challenges in federated learning for mobile edge applications, but it is incremental as it builds on existing optimization methods.
The paper tackles the problem of high training latency and low model accuracy in federated learning over mobile edge networks with constrained resources and heterogeneity by developing an online control scheme for client scheduling and resource allocation, which improves both training latency and resource efficiency compared to existing schemes.
Federated learning (FL) enables edge devices to collaboratively train a machine learning model without sharing their raw data. Due to its privacy-protecting benefits, FL has been deployed in many real-world applications. However, deploying FL over mobile edge networks with constrained resources such as power, bandwidth, and computation suffers from high training latency and low model accuracy, particularly under data and system heterogeneity. In this paper, we investigate the optimal client scheduling and resource allocation for FL over mobile edge networks under resource constraints and uncertainty to minimize the training latency while maintaining the model accuracy. Specifically, we first analyze the impact of client sampling on model convergence in FL and formulate a stochastic optimization problem that captures the trade-off between the running time and model performance under heterogeneous and uncertain system resources. To solve the formulated problem, we further develop an online control scheme based on Lyapunov-based optimization for client sampling and resource allocation without requiring the knowledge of future dynamics in the FL system. Extensive experimental results demonstrate that the proposed scheme can improve both the training latency and resource efficiency compared with the existing schemes.