Datasets detail.

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）作为一种以隐私保护为核心的医学影像处理方案，获得了广泛关注。本研究以COVIDx CXR-3数据集为案例，探究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-identically and independently distributed, non-IID）数据与独立同分布（identically and independently distributed, IID）数据上的性能表现。研究结果表明，随着数据异质性程度提升，模型性能出现显著衰减，这凸显了研发创新策略以在多样化环境中优化联邦学习的必要性。本研究推动了联邦学习的实际落地，跳出了纯理论研究的范畴，同时解决了医学影像应用中的各类细节问题。本研究揭示了数据多样性给联邦学习带来的固有挑战，为后续开发可有效应对数据异质性的联邦学习策略奠定了基础，尤其在医疗等敏感领域中具备重要应用价值。