Global accuracy using MLP.

In recent years, Federated Learning (FL) has gained traction as a privacy-centric approach in medical imaging. This study explores the challenges posed by data heterogeneity on FL algorithms, using the COVIDx CXR-3 dataset as a case study. We contrast the performance of the Federated Averaging (FedAvg) algorithm on non-identically and independently distributed (non-IID) data against identically and independently distributed (IID) data. Our findings reveal a notable performance decline with increased data heterogeneity, emphasizing the need for innovative strategies to enhance FL in diverse environments. This research contributes to the practical implementation of FL, extending beyond theoretical concepts and addressing the nuances in medical imaging applications. This research uncovers the inherent challenges in FL due to data diversity. It sets the stage for future advancements in FL strategies to effectively manage data heterogeneity, especially in sensitive fields like healthcare.

近年来，联邦学习（Federated Learning，FL）作为一种以隐私为中心的方法，在医学影像领域获得了广泛关注。本研究以COVIDx CXR-3数据集为案例，探讨数据异质性对联邦学习算法带来的挑战。我们对比了联邦平均（Federated Averaging，FedAvg）算法在非独立同分布（non-IID）数据与独立同分布（IID）数据上的性能表现。研究结果显示，随着数据异质性程度提升，模型性能出现显著下降，这凸显了在多样化场景下优化联邦学习的创新策略的必要性。本研究不仅拓展了联邦学习的理论范畴，更针对医学影像应用中的细节问题，为联邦学习的实际落地提供了实践参考。本研究揭示了因数据多样性引发的联邦学习内在挑战，为未来优化联邦学习策略以有效应对数据异质性奠定了基础，尤其在医疗这类敏感领域中具有重要意义。