简单高效的联邦学习算法在多种条件下表现稳定，适合医疗场景部署。
Revisit the Stability of Vanilla Federated Learning Under Diverse Conditions
- 对比多种联邦学习方法，发现基础FedAvg在不同设置下均表现稳定。
- 在血细胞和皮肤病变分类任务中，FedAvg性能优于复杂模型且无需调参。
- 特别适合资源有限的医院处理医疗数据，可作为临床实践的可靠基准。
联邦学习（FL）是一种分布式机器学习范式，可在保护数据隐私的前提下实现跨去中心化客户端的协同模型训练。本文重新评估了基础FedAvg算法在多样化条件下的稳定性。尽管其结构简单，但相比更先进的联邦学习技术，FedAvg展现出显著的稳定性。我们在血细胞和皮肤病变分类任务上，使用视觉变换器（ViT）评估了多种联邦学习方法的性能，并分析了不同分类模型的影响以及对超参数变化的敏感性。结果一致表明，无论数据集、分类模型或超参数设置如何，FedAvg均保持稳健表现。鉴于其稳定性与无需复杂调参的优势，FedAvg是资源受限医院处理医疗数据时安全高效的部署选择。这些发现凸显了基础版FedAvg作为临床实践中可信基线的持久价值。
原文摘要 · Abstract (English)
Federated Learning (FL) is a distributed machine learning paradigm enabling collaborative model training across decentralized clients while preserving data privacy. In this paper, we revisit the stability of the vanilla FedAvg algorithm under diverse conditions. Despite its conceptual simplicity, FedAvg exhibits remarkably stable performance compared to more advanced FL techniques. Our experiments assess the performance of various FL methods on blood cell and skin lesion classification tasks using Vision Transformer (ViT). Additionally, we evaluate the impact of different representative classification models and analyze sensitivity to hyperparameter variations. The results consistently demonstrate that, regardless of dataset, classification model employed, or hyperparameter settings, FedAvg maintains robust performance. Given its stability, robust performance without the need for extensive hyperparameter tuning, FedAvg is a safe and efficient choice for FL deployments in resource-constrained hospitals handling medical data. These findings underscore the enduring value of the vanilla FedAvg approach as a trusted baseline for clinical practice.
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