Federated learning (FL) enables collaborative model training across multiple parties without sharing raw data, with semi-asynchronous FL (SAFL) emerging as a balanced approach between synchronous and asynchronous FL. However, SAFL faces significant challenges in optimizing both gradient-based (e.g., FedSGD) and model-based (e.g., FedAvg) aggregation strategies, which exhibit distinct trade-offs in accuracy, convergence speed, and stability. While gradient aggregation achieves faster convergence and higher accuracy, it suffers from pronounced fluctuations, whereas model aggregation offers greater stability but slower convergence and suboptimal accuracy. This paper presents FedQS, the first framework to theoretically analyze and address these disparities in SAFL. FedQS introduces a divide-and-conquer strategy to handle client heterogeneity by classifying clients into four distinct types and adaptively optimizing their local training based on data distribution characteristics and available computational resources. Extensive experiments on computer vision, natural language processing, and real-world tasks demonstrate that FedQS achieves the highest accuracy, attains the lowest loss, and ranks among the fastest in convergence speed, outperforming state-of-the-art baselines. Our work bridges the gap between aggregation strategies in SAFL, offering a unified solution for stable, accurate, and efficient federated learning. The code and datasets are available at https://github.com/bkjod/FedQS_.

翻译：联邦学习（FL）允许多方在不共享原始数据的情况下进行协同模型训练，其中半异步联邦学习（SAFL）作为同步与异步联邦学习之间的平衡方案而兴起。然而，SAFL在优化基于梯度的聚合策略（如FedSGD）和基于模型的聚合策略（如FedAvg）方面面临显著挑战，这两者在准确性、收敛速度和稳定性上表现出不同的权衡。梯度聚合虽能实现更快的收敛和更高的准确性，但存在明显的波动性；而模型聚合则提供更强的稳定性，但收敛速度较慢且准确性欠佳。本文提出FedQS，这是首个从理论上分析并解决SAFL中这些差异的框架。FedQS引入一种分治策略，通过将客户端划分为四种不同类型，并根据数据分布特征和可用计算资源自适应优化其本地训练，以处理客户端异构性。在计算机视觉、自然语言处理和实际任务上的大量实验表明，FedQS实现了最高的准确性、最低的损失，并在收敛速度方面名列前茅，优于现有最先进的基线方法。我们的工作弥合了SAFL中聚合策略之间的差距，为稳定、准确且高效的联邦学习提供了统一解决方案。代码和数据集可在https://github.com/bkjod/FedQS_获取。