Federated learning (FL) promises to enable collaborative machine learning across healthcare sites whilst preserving data privacy. Practical deployment remains limited by statistical heterogeneity arising from differences in patient demographics, treatments, and outcomes, and infrastructure constraints. We introduce FedMAP, a personalised FL (PFL) framework that addresses heterogeneity through local Maximum a Posteriori (MAP) estimation with Input Convex Neural Network priors. These priors represent global knowledge gathered from other sites that guides the model while adapting to local data, and we provide a formal proof of convergence. Unlike many PFL methods that rely on fixed regularisation, FedMAP's prior adaptively learns patterns that capture complex inter-site relationships. We demonstrate improved performance compared to local training, FedAvg, and several PFL methods across three large-scale clinical datasets: 10-year cardiovascular risk prediction (CPRD, 387 general practitioner practices, 258,688 patients), iron deficiency detection (INTERVAL, 4 donor centres, 31,949 blood donors), and mortality prediction (eICU, 150 hospitals, 44,842 patients). FedMAP incorporates a three-tier design that enables participation across healthcare sites with varying infrastructure and technical capabilities, from full federated training to inference-only deployment. Geographical analysis reveals substantial equity improvements, with underperforming regions achieving up to 14.3% performance gains. This framework provides the first practical pathway for large-scale healthcare FL deployment, which ensures clinical sites at all scales can benefit, equity is enhanced, and privacy is retained.

翻译：联邦学习（FL）有望在保护数据隐私的前提下实现跨医疗机构的协作机器学习。然而，实际部署仍受限于由患者人口统计学特征、治疗方案及预后差异导致的统计异质性，以及基础设施约束。本文提出FedMAP，一种个性化联邦学习（PFL）框架，通过结合输入凸神经网络先验的局部最大后验概率（MAP）估计来应对异质性问题。这些先验表征从其他站点收集的全局知识，在适应本地数据的同时指导模型，我们提供了收敛性的形式化证明。与许多依赖固定正则化的PFL方法不同，FedMAP的先验能自适应地学习捕获复杂跨站点关系的模式。我们在三个大规模临床数据集上展示了其相对于本地训练、FedAvg及多种PFL方法的性能提升：十年心血管风险预测（CPRD，387家全科诊所，258,688名患者）、缺铁检测（INTERVAL，4个献血中心，31,949名献血者）以及死亡率预测（eICU，150家医院，44,842名患者）。FedMAP采用三层设计，支持具有不同基础设施和技术能力的医疗机构参与，涵盖从完整联邦训练到仅推理部署的多种模式。地理分析显示其显著提升了公平性，表现欠佳区域的性能提升最高达14.3%。该框架为大规模医疗联邦学习的实际部署提供了首个可行路径，确保所有规模的临床机构均能受益，同时增强公平性并保障隐私。