发表机构
Missouri University of Science and Technology; University of Alabama in Huntsville（密苏里科技大学; 阿拉巴马大学亨茨维尔分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出自适应贝叶斯伙伴选择（ABPS）框架，通过Beta-Bernoulli后验和UCB标准实现点对点协作，在230个非IID临床中心上以0.09倍通信成本匹配FedDyn性能，减少通信且不牺牲准确性。
AI 中文摘要
联邦学习（FL）在医疗保健领域面临显著的异质性和跨临床中心的时间概念漂移，其中不断变化的患者群体和护理实践会改变数据分布。现有方法依赖持续的全局通信，产生大量带宽开销，同时存在来自对齐不良同伴的负迁移风险。我们提出自适应贝叶斯伙伴选择（ABPS），这是一种点对点框架，管理谁协作、何时协作以及以何种成本协作。每个中心维护关于潜在同伴的Shapley边际效用的Beta-Bernoulli后验分布，使用上置信界（UCB）标准对候选者进行排名，并通过轻量级提议-拒绝机制形成协作，当不存在互利的伙伴时，可选择弃权（不执行）通信。该框架具有随机决策解释，提供有限样本集中保证和伙伴选择中O(kappa log T)的遗憾界，以及负迁移下故意隔离最优的条件。轻量级扩展（头部个性化、bfloat16量化通信和可调活动集大小）进一步提高效率，而目标感知元数据过滤器实现机构特定的协作策略。在ICU住院前24小时的二元院内死亡率预测中，从MIMIC-IV中抽取230个非IID临床中心，完整的ABPS-X变体以FedAvg通信成本的0.09倍匹配最强联邦基线（FedDyn，AUROC 0.758），且变异性降低。多样性驱动配置对相当一部分中心启用故意隔离。这些结果表明，当中心数量众多且规模较小时，自适应、效用感知的协作在不牺牲准确性的情况下减少通信，为医疗FL提供可扩展范式。
英文摘要
Federated learning (FL) in healthcare faces pronounced heterogeneity and temporal concept drift across clinical centers, where evolving patient populations and care practices shift data distributions. Existing approaches rely on persistent global communication, incurring substantial bandwidth overhead while risking negative transfer from poorly aligned peers. We propose Adaptive Bayesian Partner Selection (ABPS), a peer-to-peer framework that governs who collaborates, when, and at what cost. Each center maintains a Beta-Bernoulli posterior over prospective peers' Shapley marginal utility, ranks candidates with an Upper Confidence Bound (UCB) criterion, and forms collaborations through a lightweight propose-reject mechanism, with the option to abstain from communication when no mutually beneficial partner exists. The framework admits a stochastic decision interpretation, yielding finite-sample concentration guarantees and O(kappa log T) regret in partner selection, along with conditions under which intentional isolation is optimal under negative transfer. Lightweight extensions (head personalization, bfloat16 quantized communication, and a tunable active-set size) further improve efficiency, and a goal-aware metadata filter enables institution-specific collaboration strategies. On binary in-hospital mortality prediction over the first 24 hours of an ICU stay, with 230 non-IID clinical centers drawn from MIMIC-IV, the full ABPS-X variant matches the strongest federated baseline (FedDyn, AUROC 0.758) at 0.09x the communication cost of FedAvg, with reduced variability. A diversity-driven configuration activates intentional isolation for a substantial fraction of centers. These results show that adaptive, utility-aware collaboration reduces communication without sacrificing accuracy when centers are numerous and small, offering a scalable paradigm for healthcare FL.
Comments19 pages, 4 figures
