标题： 揭开联邦学习在医学图像分类中的优化误区 显示英文标题

标题： Debunking Optimization Myths in Federated Learning for Medical Image Classification

摘要： 联邦学习（FL）是一种协作学习方法，在保持数据隐私的同时实现去中心化的模型训练。 尽管在医学影像方面具有潜力，但最近的FL方法通常对局部因素（如优化器和学习率）敏感，这限制了它们在实际部署中的鲁棒性。 在本工作中，我们重新审视原始FL，以明确边缘设备配置的影响，并在结直肠病理学和血细胞分类任务上对最近的FL方法进行基准测试。 我们通过数值实验表明，局部优化器和学习率的选择对性能的影响大于具体的FL方法。 此外，我们发现增加局部训练轮次可能会增强或损害收敛，具体取决于FL方法。 这些发现表明，适当的边缘特定配置比算法复杂性对于实现有效的FL更为关键。 显示英文摘要

摘要： Federated Learning (FL) is a collaborative learning method that enables decentralized model training while preserving data privacy. Despite its promise in medical imaging, recent FL methods are often sensitive to local factors such as optimizers and learning rates, limiting their robustness in practical deployments. In this work, we revisit vanilla FL to clarify the impact of edge device configurations, benchmarking recent FL methods on colorectal pathology and blood cell classification task. We numerically show that the choice of local optimizer and learning rate has a greater effect on performance than the specific FL method. Moreover, we find that increasing local training epochs can either enhance or impair convergence, depending on the FL method. These findings indicate that appropriate edge-specific configuration is more crucial than algorithmic complexity for achieving effective FL.