Fed-Equilibrium框架：面向鲁棒且公平的临床联邦学习中的拓扑帕累托控制
Fed-Equilibrium Framework for Topological Pareto Control in Robust and Fair Clinical Federated Learning
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中文总结 AI 辅助
提出Fed-Equilibrium框架，通过两阶段梯度控制实现拓扑均衡，解决临床联邦学习中的知识主导问题，在跨国模拟中兼顾鲁棒性与公平性，使少数节点达到深度收敛。
中文摘要 AI 辅助
联邦学习（FL）在多中心临床网络中的部署面临“知识主导”的挑战，即高数据量的中心节点自然压过少数群体节点，隐含地将较小队列的独特临床模式视为异常值。现有的几何防御方法提供了安全基线，但未能解决这一效率与公平性的两难问题。为弥合这一差距，我们提出了Fed-Equilibrium，一个将范式从简单防御推进到拓扑均衡的框架。与传统聚合器不同，Fed-Equilibrium实现了顺序架构协同。它采用两阶段梯度控制级联：第一阶段（几何质量保证）通过余弦相似度漏斗强制方向一致性以过滤恶意噪声，从而创建稳定的流形；第二阶段（拓扑帕累托控制）通过识别最优帕累托拐点主动调节经过验证的贡献。我们在一个跨国模拟中验证了该框架，整合了加拿大（CNODES）和美国（SyntheticMass）注册数据。实验结果表明，该系统在保护网络免受对抗性发散的同时，能够容纳代表性不足的信号。值得注意的是，占数据量不足3%的美国少数群体节点（spoke）实现了与数据丰富的加拿大中心节点相当的深度收敛。这证实了Fed-Equilibrium有效对抗“知识主导”，建立了真正的“知识共享”，其中全局泛化性不以牺牲本地临床代表性为代价。
英文摘要
The deployment of Federated Learning (FL) in multi-center clinical networks faces the challenge of "knowledge dominance," where high-volume hubs naturally overwhelm minority community nodes, implicitly treating the distinct clinical patterns of smaller cohorts as outliers. Existing geometric defenses provide a security baseline but leave this efficiency-fairness dilemma unresolved. To bridge this gap, we propose Fed-Equilibrium, a framework that advances the paradigm from simple defense to topological equilibrium. Unlike traditional aggregators, Fed-Equilibrium implements a sequential architectural synergy. It utilizes a two-stage gradient control cascade: Stage I (geometric quality assurance) enforces directional consistency via a cosine similarity funnel to filter malicious noise, creating a stabilized manifold; Stage II (topological Pareto control) then actively modulates verified contributions by identifying the optimal Pareto knee point. We validated this framework on a bi-national simulation integrating Canadian (CNODES) and U.S. (SyntheticMass) registries. Experimental results demonstrate that the system simultaneously secures the network against adversarial divergence while accommodating underrepresented signals. Notably, the minority U.S. spoke (representing less than 3% of data volume) achieved deep convergence comparable to the data-rich Canadian hub. This confirms that Fed-Equilibrium effectively counters "knowledge dominance," establishing a true "knowledge commons" where global generalizability does not come at the cost of local clinical representation.
