实证发现联邦学习性能更依赖设备配置而非算法复杂度。
Debunking Optimization Myths in Federated Learning for Medical Image Classification
- 对比多种联邦学习方法，发现本地优化器和学习率影响更大。
- 增加本地训练轮次可能提升或降低收敛效果，取决于具体方法。
- 适合医疗图像分类场景下部署联邦学习的开发者参考。
联邦学习（FL）是一种在保护数据隐私的前提下实现分布式模型训练的协作学习方法。尽管在医学影像领域前景广阔，但现有联邦学习方法常对本地配置（如优化器、学习率）敏感，限制了实际部署中的鲁棒性。本文重新审视基础联邦学习，通过在结直肠病理和血细胞分类任务上基准测试近期方法，量化表明本地优化器与学习率的选择对性能的影响大于具体联邦学习算法本身。此外，增加本地训练轮次的效果具有双面性：根据所用方法不同，可能促进或损害收敛。结果表明，在实际应用中，合理配置边缘设备参数比追求算法复杂度更为关键。
原文摘要 · Abstract (English)
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.
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