CSFL: Communication-Efficient Semi-Asynchronous Federated Learning Method in Resource-Constrained Edge Computing

Abstract

Federated learning (FL) is a distributed machine learning (ML) paradigm that has been widely used to train ML models on massive amounts of data in edge computing (EC) environments. However, FL faces significant challenges from device heterogeneity, edge dynamics, and limited communication resources. To address these challenges, we propose a communication-efficient semi-asynchronous FL (CSFL) framework. First, the work introduces a threshold adaptive gradient compression (TAGC) algorithm, which can reduce redundant communication rounds and accelerate model convergence by appropriately increasing local computation. Second, we propose an adaptive weight adjustment mechanism (AWAM), which employs a staleness-based decay function and, based on varying data distributions, sets different weight coefficients to mitigate the impact of statistical and system heterogeneity. To tackle edge dynamics, a dynamic node selection algorithm based on deep reinforcement learning (DRL) is proposed. This algorithm enables adaptive adjustment of the number of local models participating in global model aggregation according to environmental changes. Finally, we analyze the convergence bound of CSFL theoretically and conduct extensive experiments on classical datasets to demonstrate the effectiveness of our algorithm. Compared with baseline algorithms, the experimental results indicate that CSFL can effectively decrease bandwidth resource consumption and total training time during the training of edge intelligence models across various datasets and data distributions.