Data privacy and security is an essential challenge in medical clinical settings, where individual hospital has its own sensitive patients data. Due to recent advances in decentralized machine learning in Federated Learning (FL), each hospital has its own private data and learning models to collaborate with other trusted participating hospitals. Heterogeneous data and models among different hospitals raise major challenges in robust FL, such as gradient leakage, where participants can exploit model weights to infer data. Here, we proposed a robust FL method to efficiently tackle data and model heterogeneity, where we train our model using knowledge distillation and a novel weighted client confidence score on hematological cytomorphology data in clinical settings. In the knowledge distillation, each participant learns from other participants by a weighted confidence score so that knowledge from clean models is distributed other than the noisy clients possessing noisy data. Moreover, we use symmetric loss to reduce the negative impact of data heterogeneity and label diversity by reducing overfitting the model to noisy labels. In comparison to the current approaches, our proposed method performs the best, and this is the first demonstration of addressing both data and model heterogeneity in end-to-end FL that lays the foundation for robust FL in laboratories and clinical applications.

数据隐私和安全是医疗临床环境中的一个重要挑战，各个医院都有自己的敏感患者数据。由于联邦学习（FL）中去中心化机器学习的最新进展，每家医院都有自己的私有数据和学习模型，可以与其他值得信赖的参与医院合作。不同医院之间的异构数据和模型给鲁棒 FL 带来了重大挑战，例如梯度泄漏，参与者可以利用模型权重来推断数据。在这里，我们提出了一种稳健的 FL 方法来有效地处理数据和模型异质性，其中我们使用知识蒸馏和临床环境中血液细胞形态学数据的新型加权客户置信度来训练我们的模型。在知识蒸馏中，每个参与者通过加权置信度得分向其他参与者学习，以便将来自干净模型的知识分布到拥有噪声数据的噪声客户端之外。此外，我们使用对称损失通过减少模型对噪声标签的过度拟合来减少数据异质性和标签多样性的负面影响。与当前的方法相比，我们提出的方法表现最好，这是解决端到端 FL 中数据和模型异质性的首次演示，为实验室和临床应用中稳健的 FL 奠定了基础。