ASFL: Adaptive Semi-asynchronous Federated Learning for Balancing Model Accuracy and Total Latency in Mobile Edge Networks

Jieling Yu, Ruiting Zhou, Chen Chen, Bo Li, Fang Dong

Abstract: Federated learning (FL) is a new paradigm for privacy-preserving learning. This is particularly appealing in the mobile edge network (MEN), in which devices collectively train a global model with their own set of data. It is, however, routinely difficult for FL algorithms to satisfy different training task preferences in terms of the total latency and model accuracy due to a number of factors including the straggler effect, data heterogeneity, communication bottleneck and device mobility. To this end, we propose an Adaptive Semi-asynchronous Federated Learning (ASFL) framework, which adaptively balances the total latency and model accuracy according to the task preferences in MEN. Specifically, ASFL conducts a two-stage operation: i) Device selection stage. Each global round selects a set of devices that can maximize the model accuracy to eliminate data heterogeneity and communication bottlenecks; ii) Training stage. We first define a latency-accuracy objective value to model the balance between the latency and accuracy. Then in each global round, we use a deep reinforcement learning (DRL) algorithm based on soft actor-critic with discrete actions to intelligently derive the number of picked devices (i.e., participants in the current global aggregation) and the lag tolerance at each global round to maximize the latency-accuracy objective value. Extensive experiments show that ASFL can improve the latency-accuracy objective value by up to 94% compared with three state-of-the-art FL frameworks.