发表机构
Federal Institute of Education, Science and Technology of Rio Grande do Norte (IFRN)（北里奥格兰德联邦教育、科学与技术研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究在五个真实心血管数据集上验证FedCVR框架，通过异构联邦场景和留一机构交叉验证，证明其在实际数据上保持自适应优势，F1分数达79.2%，AUC为0.96，优于标准FedAvg，验证了临床可行性。
AI 中文摘要
在真实临床数据上验证联邦学习框架是从可控合成环境中的概念验证演示到真实多中心医疗保健环境中部署的关键步骤。同一作者之前的架构研究在合成六特征基准上表明，服务器端自适应优化可作为差分隐私噪声的时间去噪器。本研究通过在五个公开可用的真实心血管数据集上验证FedCVR框架来填补这一空白，结果表明FedCVR在实际数据上保持其自适应优势，在操作隐私预算下F1分数达到79.2%，AUC为0.96，且在所有评估指标上统计上优于标准FedAvg，证实了该框架在真实多中心环境中的临床可行性。
英文摘要
Validating federated learning frameworks on real clinical data is an essential step between proof-of-concept demonstrations in controlled synthetic environments and deployment in real multicenter healthcare settings. A prior architectural study by the same authors (Tertulino and Alencar, 2026) demonstrated, on a synthetic six-feature benchmark, that server-side adaptive optimization acts as a temporal denoiser for Differential Privacy noise, answering an open challenge identified in the original pipeline work (Tertulino, 2025). That study used synthetically generated data and explicitly identified real-world validation as a priority future direction. The present work addresses this gap by validating the FedCVR framework on five publicly available real cardiovascular datasets (Framingham, Cleveland, Hungarian, Switzerland, and Long Beach VA), harmonized to the 13-attribute UCI Heart Disease schema and configured as a heterogeneous federated scenario with leave-one-institution-out cross-validation. Results demonstrate that FedCVR preserves its adaptive advantage on real data, achieving an F1-Score of 79.2% and AUC of 0.96 under the operational privacy budget (noise multiplier = 0.8, privacy budget epsilon approximately 4.2), while statistically outperforming standard FedAvg on all evaluated metrics (paired t-tests, all p <= 0.003, significant under the Bonferroni-corrected threshold). The measured privacy cost on real data confirms the graceful degradation pattern observed in the synthetic experiments, providing empirical evidence of the framework's clinical viability in genuine multicenter contexts.
Comments16 pages, 3 figures. Submitted to the Journal of the Brazilian Computer Society (JBCS)
