SACW: Semi-Asynchronous Federated Learning with Client Selection and Adaptive Weighting

Federated learning (FL), as a privacy-preserving distributed machine learning paradigm, demonstrates unique advantages in addressing data silo problems. However, the prevalent statistical heterogeneity (data distribution disparities) and system heterogeneity (device capability variations) in practical applications significantly hinder FL performance. Traditional synchronous FL suffers from severe waiting delays due to its mandatory synchronization mechanism, while asynchronous approaches incur model bias issues caused by training pace discrepancies. To tackle these challenges, this paper proposes the SACW framework, which effectively balances training efficiency and model quality through a semi-asynchronous training mechanism. The framework adopts a hybrid strategy of “asynchronous client training–synchronous server aggregation,” combined with an adaptive weighting algorithm based on model staleness and data volume. This approach significantly improves system resource utilization and mitigates system heterogeneity. Simultaneously, the server employs data distribution-aware client clustering and hierarchical selection strategies to construct a training environment characterized by “inter-cluster heterogeneity and intra-cluster homogeneity.” Representative clients from each cluster are selected to participate in model aggregation, thereby addressing data heterogeneity. We conduct comprehensive comparisons with mainstream synchronous and asynchronous FL methods and perform extensive experiments across various model architectures and datasets. The results demonstrate that SACW achieves better performance in both training efficiency and model accuracy under scenarios with system and data heterogeneity.