在真实心脏数据上验证了联邦学习的隐私保护效果，显著提升诊断准确率。
Recovering Clinical Utility Under Differential Privacy: Empirical Validation of Adaptive Federated Aggregation on Heterogeneous Cardiovascular Datasets
- 通过自适应聚合机制减少差分隐私噪声干扰，提升模型性能。
- 在五大数据集上实现79.2%的F1分数和0.96的AUC，优于传统方法。
- 适合医疗联邦学习研究者与隐私保护系统开发者参考。
在真实临床数据上验证联邦学习框架是迈向多中心医疗部署的关键一步。本文在五个公开的心血管数据集（弗雷明汉、克利夫兰、匈牙利、瑞士、长滩退伍军人医院）上评估了FedCVR框架，这些数据统一为13属性的UCI心脏病标准格式，并设置为异构联邦场景，采用留一机构交叉验证。结果表明，该框架在真实数据中仍保持自适应优势，在操作级隐私预算下（噪声倍数=0.8，隐私预算ε≈4.2），实现了79.2%的F1分数和0.96的AUC，且在所有指标上均显著优于标准FedAvg（配对t检验，所有p≤0.003，经邦弗朗尼校正后仍显著）。实测隐私成本呈现平滑下降趋势，验证了该框架在真实多中心环境中的临床可行性。
原文摘要 · Abstract (English)
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.
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