Automated blood morphology analysis can support hematological diagnostics in low- and middle-income countries (LMICs) but remains sensitive to dataset shifts from staining variability, imaging differences, and rare morphologies. Building centralized datasets to capture this diversity is often infeasible due to privacy regulations and data-sharing restrictions. We introduce a federated learning framework for white blood cell morphology analysis that enables collaborative training across institutions without exchanging training data. Using blood films from multiple clinical sites, our federated models learn robust, domain-invariant representations while preserving complete data privacy. Evaluations across convolutional and transformer-based architectures show that federated training achieves strong cross-site performance and improved generalization to unseen institutions compared to centralized training. These findings highlight federated learning as a practical and privacy-preserving approach for developing equitable, scalable, and generalizable medical imaging AI in resource-limited healthcare environments.

翻译：自动化血液形态学分析可为中低收入国家（LMICs）的血液学诊断提供支持，但其对染色差异、成像差异及罕见形态学变化引起的数据集偏移仍较为敏感。由于隐私法规和数据共享限制，构建集中式数据集以捕获这种多样性通常不可行。我们提出了一种用于白细胞形态学分析的联邦学习框架，该框架能够在无需交换训练数据的情况下实现跨机构协作训练。利用来自多个临床站点的血涂片，我们的联邦模型能够学习鲁棒的、领域不变的表示，同时保持数据的完全隐私。在基于卷积和Transformer的架构上进行评估表明，与集中式训练相比，联邦训练实现了强大的跨站点性能，并对未见机构具有更好的泛化能力。这些发现凸显了联邦学习作为一种实用且保护隐私的方法，可在资源有限的医疗环境中开发公平、可扩展且可泛化的医学影像人工智能。