Federated learning is highly valued due to its high-performance computing in distributed environments while safeguarding data privacy. To address resource heterogeneity, researchers have proposed a semi-asynchronous federated learning (SAFL) architecture. However, the performance gap between different aggregation targets in SAFL remain unexplored. In this paper, we systematically compare the performance between two algorithm modes, FedSGD and FedAvg that correspond to aggregating gradients and models, respectively. Our results across various task scenarios indicate these two modes exhibit a substantial performance gap. Specifically, FedSGD achieves higher accuracy and faster convergence but experiences more severe fluctuates in accuracy, whereas FedAvg excels in handling straggler issues but converges slower with reduced accuracy.

翻译：联邦学习因其在分布式环境中实现高性能计算并保障数据隐私而备受重视。为应对资源异构性问题，研究者提出了半异步联邦学习架构。然而，该架构中不同聚合目标之间的性能差异尚未得到充分探索。本文系统比较了分别对应梯度聚合与模型聚合的两种算法模式——FedSGD与FedAvg的性能表现。我们在多种任务场景下的实验结果表明，这两种模式存在显著的性能差异。具体而言，FedSGD能获得更高的精度与更快的收敛速度，但其精度波动更为剧烈；而FedAvg在应对掉队者问题上表现优异，但收敛速度较慢且最终精度较低。