摘要：在真实临床数据上验证联邦学习框架，是从受控合成环境中的概念验证演示迈向真实多中心医疗部署之间的关键一步。同一作者此前的一项架构研究（Tertulino and Alencar，2026）在一个包含六项特征的合成基准上表明，服务器端自适应优化可充当差分隐私噪声的时间去噪器，从而回应了原始流水线研究（Tertulino，2025）所指出的一项开放性挑战。该研究采用合成生成数据，并明确将真实世界验证列为优先的未来研究方向。本研究通过在五个公开可用的真实心血管数据集（Framingham、Cleveland、Hungarian、Switzerland 和 Long Beach VA）上验证 FedCVR 框架来填补这一空白。这些数据集被统一至包含 13 项属性的 UCI 心脏病模式，并被配置为采用留一机构交叉验证的异质联邦学习场景。结果表明，FedCVR 在真实数据上仍保留其自适应优势：在运行隐私预算设置下（噪声乘数 = 0.8，隐私预算 epsilon 约为 4.2），实现了 79.2% 的 F1-Score 和 0.96 的 AUC；同时，在所有评估指标上均统计显著优于标准 FedAvg（配对 t 检验，所有 p <= 0.003，在 Bonferroni 校正阈值下显著）。在真实数据上测得的隐私代价证实了合成实验中观察到的平缓退化模式，为该框架在真实多中心场景中的临床可行性提供了实证证据。

Abstract: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.