FedImp: Federated Learning Using Important Layers of Client Models for the Diagnosis of Breast Cancer Histopathology Images

Abstract

Federated learning methods can utilize datasets from multiple clients without requiring to share the data. Thus, these methods are helpful in preserving privacy and taking the benefit of a larger pool of data. However, most federated learning methods do not explicitly consider the importance of different layers of client models in decision-making. We propose FedImp, a federated learning method that utilizes important layers from client models for the diagnosis of breast cancer histopathology images. Our method utilizes the important layers of the client models for designing the loss function for training the client models. This may allow the client models to adapt to the data heterogeneity across clients. The important layers of client models are also utilized for aggregating the client models in the central server. Our approach limits the deviation of the client models from each other and from the aggregated model at the central server. This is likely to help in alleviating the deviation in decision-making across clients. The performance of our method is evaluated on publicly available breast cancer histopathology datasets from multiple sources. Experiments show as high as 7% increase in AUROC scores compared to state-of-the-art approaches. The code is available at https://github.com/deepMB/FedImp.