Artificial intelligence (AI) shows great promise in revolutionizing medical imaging, improving diagnosis, and refining treatment methods. However, the training of AI models relies on extensive multi-center datasets, presenting a potential challenge due to concerns about data privacy protection. Federated learning offers a solution by enabling a collaborative model across multiple centers without sharing raw data. In this study, we present a Federated Attention Contrastive Learning (FACL) framework designed to address challenges associated with large-scale pathological images and data heterogeneity. FACL improves model generalization by maximizing attention consistency between the local client and the server model. To enhance privacy and validate robustness, we incorporate differential privacy by introducing noise during parameter transfer. We assess the effectiveness of FACL in cancer diagnosis and Gleason grading tasks using 19,461 whole slide images of prostate cancer sourced from multiple centers. In the diagnosis task, FACL achieves an area under the curve (AUC) of 0.9718, outperforming seven centers whose average AUC is 0.9499 when categories are relatively balanced. In 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.

翻译：人工智能（AI）在革新医学影像、改善诊断以及优化治疗方法方面展现出巨大潜力。然而，AI模型的训练依赖于大规模多中心数据集，而数据隐私保护问题对此构成了潜在挑战。联邦学习通过允许多中心在不共享原始数据的情况下协作训练模型，为这一问题提供了解决方案。在本研究中，我们提出了一种联邦注意力对比学习（FACL）框架，旨在应对大规模病理图像及数据异质性带来的挑战。FACL通过最大化本地客户端与服务器模型之间的注意力一致性来提升模型泛化能力。为了增强隐私保护并验证鲁棒性，我们在参数传输过程中引入差分隐私，通过添加噪声实现。我们使用来自多个中心的19,461张前列腺癌全切片图像，评估了FACL在癌症诊断和Gleason分级任务中的有效性。在诊断任务中，当类别相对平衡时，FACL的曲线下面积（AUC）达到0.9718，优于七个中心的平均AUC（0.9499）。在Gleason分级任务中，FACL的Kappa分数为0.8463，超过六个中心的平均Kappa分数（0.7379）。综上所述，FACL为前列腺癌病理诊断提供了一种鲁棒、准确且经济高效的AI训练模型，同时有效维护了数据安全。