Adaptive Bayesian Partner Selection for Federated Clinical Centers
Read the original on arXiv Machine Learning →Adaptive Bayesian Partner Selection (ABPS) is a peer‑to‑peer federated learning framework designed for heterogeneous clinical centers, where each center maintains a Beta‑Bernoulli posterior over prospective peers’ Shapley marginal utility and selects partners using an Upper Confidence Bound criterion. The lightweight propose‑reject mechanism allows centers to collaborate only when mutually beneficial, with the option to abstain from communication entirely. In experiments on 230 non‑IID ICU centers predicting in‑hospital mortality, the ABPS‑X variant achieves comparable accuracy to the strongest baseline (FedDyn) while reducing communication cost by 90% and enabling intentional isolation for many centers.
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