解决罕见病诊断中数据少、隐私难、设备弱的难题。
Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
- 用元学习实现少量样本下的联邦优化，提升泛化能力。
- 客户端调度节能降损，使掉线率降低超50%。
- 融合差分隐私保护隐私，适合临床真实场景部署。
罕见病诊断是数字健康领域最紧迫的挑战之一，受限于极端的数据稀缺性、隐私担忧以及边缘设备资源有限。本文提出自适应联邦少样本罕见病诊断框架（AFFR），整合三大核心：(i) 基于元学习的少样本联邦优化，从有限患者样本中实现泛化；(ii) 能量感知客户端调度，缓解设备掉线问题，保障参与均衡；(iii) 经校准的差分隐私安全聚合，保护敏感模型更新。与以往仅孤立处理某一问题的工作不同，AFFR将三者统一为可部署于真实临床网络的模块化流程。在模拟罕见病检测数据集上的实验表明，相比基线联邦学习，准确率最高提升10%，客户掉线率降低超过50%且不损害收敛性。此外，隐私-效用权衡仍处于临床可接受范围。结果表明，AFFR为罕见病公平可信的联邦诊断提供了可行路径。
原文摘要 · Abstract (English)
Rare-disease diagnosis remains one of the most pressing challenges in digital health, hindered by extreme data scarcity, privacy concerns, and the limited resources of edge devices. This paper proposes the Adaptive Federated Few-Shot Rare-Disease Diagnosis (AFFR) framework, which integrates three pillars: (i) few-shot federated optimization with meta-learning to generalize from limited patient samples, (ii) energy-aware client scheduling to mitigate device dropouts and ensure balanced participation, and (iii) secure aggregation with calibrated differential privacy to safeguard sensitive model updates. Unlike prior work that addresses these aspects in isolation, AFFR unifies them into a modular pipeline deployable on real-world clinical networks. Experimental evaluation on simulated rare-disease detection datasets demonstrates up to 10% improvement in accuracy compared with baseline FL, while reducing client dropouts by over 50% without degrading convergence. Furthermore, privacy-utility trade-offs remain within clinically acceptable bounds. These findings highlight AFFR as a practical pathway for equitable and trustworthy federated diagnosis of rare conditions.
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