While federated learning is a promising approach for training deep learning models over distributed sensitive datasets, it presents new challenges for machine learning, especially when applied in the medical domain where multi-centric data heterogeneity is common. Building on previous domain adaptation works, this paper proposes a novel federated learning approach for deep learning architectures via the introduction of local-statistic batch normalization (BN) layers, resulting in collaboratively-trained, yet center-specific models. This strategy improves robustness to data heterogeneity while also reducing the potential for information leaks by not sharing the center-specific layer activation statistics. We benchmark the proposed method on the classification of tumorous histopathology image patches extracted from the Camelyon16 and Camelyon17 datasets. We show that our approach compares favorably to previous state-of-the-art methods, especially for transfer learning across datasets.

本文通过引入局部统计批量规范化（BN）层，提出了一种新的联邦学习方法，使得深度学习架构能够进行协作训练，但同时具有针对多中心数据异质性的鲁棒性和不共享中心特定层激活统计信息以降低信息泄漏风险的能力。此方法在 Camelyon16 和 Camelyon17 数据集的肿瘤组织学图像块分类中表现出较好的传输学习效果，相对于之前现有技术并有很大的优势。

多中心组织病理学数据的分散联邦学习