  Artificial intelligence (AI) holds significant promise in transforming
medical imaging, enhancing diagnostics, and refining treatment strategies.
However, the reliance on extensive multicenter datasets for training AI models
poses challenges due to privacy concerns. Federated learning provides a
solution by facilitating collaborative model training across multiple centers
without sharing raw data. This study introduces a federated
attention-consistent learning (FACL) framework to address challenges associated
with large-scale pathological images and data heterogeneity. FACL enhances
model generalization by maximizing attention consistency between local clients
and the server model. To ensure privacy and validate robustness, we
incorporated differential privacy by introducing noise during parameter
transfer. We assessed the effectiveness of FACL in cancer diagnosis and Gleason
grading tasks using 19,461 whole-slide images of prostate cancer from multiple
centers. In the diagnosis task, FACL achieved an area under the curve (AUC) of
0.9718, outperforming seven centers with an average AUC of 0.9499 when
categories are relatively balanced. For the Gleason grading task, FACL attained
a Kappa score of 0.8463, surpassing the average Kappa score of 0.7379 from six
centers. In conclusion, FACL offers a robust, accurate, and cost-effective AI
training model for prostate cancer pathology while maintaining effective data
safeguards.
