AI 中文总结
针对医疗联邦的非IID数据、异构目标与私有特征问题，提出FedCARE框架，通过两阶段训练策略实现多目标个性化，在MIMIC-III等数据集上较FedAvg取得显著性能提升。
AI 中文摘要
联邦学习（FL）可在不集中敏感患者数据的前提下，实现分布式医疗机构间的协作模型训练。然而，现实中的医疗联邦不仅存在非独立同分布（non-IID）数据，还存在异构临床目标与部分重叠的特征空间：不同医院可能优化不同甚至冲突的目标，如死亡率风险预测、再入院率降低或住院时长估计，同时保留无法与其他参与者共享的机构特有临床特征。现有个性化FL方法主要解决统计异质性，而多目标FL方法通常学习共享全局模型，未显式实现客户端级适配。为解决这些局限，本文提出FedCARE——面向智慧医疗服务的多目标个性化FL框架，采用两阶段训练策略：第一阶段，通过帕累托驱动的多目标联邦优化，从共有临床特征中学习共享全局骨干；第二阶段，各客户端利用自身私有特征与本地临床目标独立微调共享骨干，在无额外通信开销的前提下实现机构特有个性化。本文在墨尔本研究云上的基于云的客户端-服务器联邦部署中实现FedCARE，并在两个真实医疗数据集MIMIC-III和美国130家医院糖尿病数据集上进行评估。实验结果表明，FedCARE的性能始终优于标准FL、多目标FL及个性化FL基线，较FedAvg实现了最高12.5%的AUROC提升与32.0%的MAE降低。
英文摘要
Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without centralising sensitive patient data. However, real-world healthcare federations are often characterised not only by non-IID data, but also by heterogeneous clinical objectives and partially overlapping feature spaces. Different hospitals may optimise distinct and potentially conflicting objectives, such as mortality risk prediction, readmission reduction, or length-of-stay estimation, while also retaining institution-specific clinical features that cannot be shared with other participants. Existing personalised FL methods mainly address statistical heterogeneity, whereas multi-objective FL approaches typically learn a shared global model without explicit client-level adaptation. To address these limitations, we propose \textbf{FedCARE}, a multi-objective personalised FL framework for smart healthcare services. FedCARE follows a two-stage training strategy. First, it learns a shared global backbone from common clinical features using Pareto-driven multi-objective federated optimisation. Second, each client independently fine-tunes the shared backbone using its private features and local clinical objectives, enabling institution-specific personalisation without additional communication overhead. We implement FedCARE in a cloud-based client-server federated deployment on the Melbourne Research Cloud and evaluate it on two real-world healthcare datasets, MIMIC-III and Diabetes 130-US Hospitals. Experimental results show that FedCARE consistently outperforms standard FL, multi-objective FL, and personalised FL baselines, achieving up to 12.5% AUROC improvement and 32.0% MAE reduction over FedAvg.
