In asynchronous federated learning (FL), client devices send updates to a central server at varying times based on their computational speed, often using stale versions of the global model. This staleness can degrade the convergence and accuracy of the global model. Previous work, such as AsyncFedED, proposed an adaptive aggregation method using Euclidean distance to measure staleness. In this paper, we extend this approach by exploring alternative distance metrics to more accurately capture the effect of gradient staleness. We integrate these metrics into the aggregation process and evaluate their impact on convergence speed, model performance, and training stability under heterogeneous clients and non-IID data settings. Our results demonstrate that certain metrics lead to more robust and efficient asynchronous FL training, offering a stronger foundation for practical deployment.

翻译：在异步联邦学习（FL）中，客户端设备根据其计算速度在不同时间向中央服务器发送更新，这些更新通常基于全局模型的陈旧版本。这种陈旧性会降低全局模型的收敛速度和精度。先前的研究，如AsyncFedED，提出了一种使用欧氏距离来度量陈旧性的自适应聚合方法。本文通过探索替代的距离度量来扩展这一方法，以更准确地捕捉梯度陈旧性的影响。我们将这些度量整合到聚合过程中，并在异构客户端和非独立同分布数据设置下评估它们对收敛速度、模型性能和训练稳定性的影响。我们的结果表明，某些度量能够带来更稳健和高效的异步联邦学习训练，为实际部署提供了更坚实的基础。