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arXiv Machine Learning By Ting Xu, Henry Leung

Fed-Equilibrium Framework for Topological Pareto Control in Robust and Fair Clinical Federated Learning

Read the original on arXiv Machine Learning →

Fed-Equilibrium is a federated learning framework designed to address the knowledge dominance problem in multi-center clinical networks, where high-volume hubs overpower minority community nodes. It employs a two-stage gradient control cascade: Stage I uses a cosine similarity funnel for geometric quality assurance, while Stage II applies topological Pareto control to identify an optimal knee point for contribution modulation. Experiments on Canadian and U.S. registries show that the framework secures the network against adversarial divergence and enables minority U.S. nodes—comprising less than 3% of the data—to achieve deep convergence comparable to the data-rich Canadian hub.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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