  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.
