Privacy-preserving federated learning with light-weight attention improved CNNs for automated leukemia detection across distributed medical imaging.

This research work describes a lightweight, secure, and interpretable federated learning framework for automatic leukemia classification, which identifies and addresses various problems regarding clinical data security and collaborative model building among partnering healthcare organizations. This framework employs a distributed learning paradigm that allows a number of healthcare facilities to work together to build a high predictive performance classification model while training the model without exchanging sensitive information about patient data, thus ensuring data privacy and methodological reproducibility. The proposed framework employs a lightweight attention-enhanced convolutional neural network (CNN) for the automated classification of leukemia cells to one of the four categories: benign, early, pre-leukemic, and pro-leukemic at only 0.14 s/batch. The global model at 3 clients achieves 95.70% test accuracy while at 5 clients and increased training rounds achieve 96.56% on test set on a weighted aggregation method. Additionally, for increased clinical interpretability and transparency explainable methods are used in this study.