解决医疗AI联邦学习的公平性与可扩展性难题，提升小机构参与度。
Beyond Static Knowledge Messengers: Towards Adaptive, Fair, and Scalable Federated Learning for Medical AI
- 动态调整知识传递机制，适应不同机构数据差异
- 收敛速度提升60-70%，支持超100家机构协同
- 兼顾隐私合规与多模态数据融合，适合医疗协作场景
医疗AI在保护隐私的前提下实现跨机构协作面临挑战，现有联邦学习方法存在架构僵化、收敛慢（45-73轮）、小机构公平性差、可扩展性弱（限15客户端）等问题。本文提出自适应公平联邦学习（AFFL），通过三项创新：（1）自适应知识使者根据异质性和任务复杂度动态调节容量；（2）基于影响权重聚合的公平性感知蒸馏；（3）课程引导加速，使迭代轮次减少60-70%。理论分析表明，收敛速率可达O(T^{-1/2}) + O(H_max/T^{3/4})，具备epsilon公平性保证。预计通信量减少55-75%，公平性提升56-68%，能耗下降34-46%，支持100+机构。框架支持影像、基因组、电子病历、传感器等多模态数据整合，符合HIPAA/GDPR要求。提出MedFedBench基准套件，涵盖六项评估维度：收敛效率、机构公平性、隐私保护、多模态融合、可扩展性、临床部署能力。经济测算显示，乡村医院投资回报率可达400-800%，学术中心性能提升15-25%。本文还提出七问研究议程、24个月实施路线图，推动医疗AI普惠化。
原文摘要 · Abstract (English)
Medical AI faces challenges in privacy-preserving collaborative learning while ensuring fairness across heterogeneous healthcare institutions. Current federated learning approaches suffer from static architectures, slow convergence (45-73 rounds), fairness gaps marginalizing smaller institutions, and scalability constraints (15-client limit). We propose Adaptive Fair Federated Learning (AFFL) through three innovations: (1) Adaptive Knowledge Messengers dynamically scaling capacity based on heterogeneity and task complexity, (2) Fairness-Aware Distillation using influence-weighted aggregation, and (3) Curriculum-Guided Acceleration reducing rounds by 60-70%. Our theoretical analysis provides convergence guarantees with epsilon-fairness bounds, achieving O(T^{-1/2}) + O(H_max/T^{3/4}) rates. Projected results show 55-75% communication reduction, 56-68% fairness improvement, 34-46% energy savings, and 100+ institution support. The framework enables multi-modal integration across imaging, genomics, EHR, and sensor data while maintaining HIPAA/GDPR compliance. We propose MedFedBench benchmark suite for standardized evaluation across six healthcare dimensions: convergence efficiency, institutional fairness, privacy preservation, multi-modal integration, scalability, and clinical deployment readiness. Economic projections indicate 400-800% ROI for rural hospitals and 15-25% performance gains for academic centers. This work presents a seven-question research agenda, 24-month implementation roadmap, and pathways toward democratizing healthcare AI.
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