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A Robust Framework for Secure Cardiovascular Risk Prediction: An Architectural Case Study of Differentially Private Federated Learning

Rodrigo Tertulino, Laércio Alencar

Abstract

Accurate cardiovascular risk prediction is crucial for preventive healthcare; however, the development of robust Artificial Intelligence (AI) models is hindered by the fragmentation of clinical data across institutions due to stringent privacy regulations. This paper presents a comprehensive architectural case study validating the engineering robustness of FedCVR, a privacy-preserving Federated Learning framework applied to heterogeneous clinical networks. Rather than proposing a new theoretical optimizer, this work focuses on a systems engineering analysis to quantify the operational trade-offs of server-side adaptive optimization under utility-prioritized Differential Privacy (DP). By conducting a rigorous stress test in a high-fidelity synthetic environment calibrated against real-world datasets (Framingham, Cleveland), we systematically evaluate the system's resilience to statistical noise. The validation results demonstrate that integrating server-side momentum as a temporal denoiser allows the architecture to achieve a stable F1-score of 0.84 and an Area Under the Curve (AUC) of 0.96, statistically outperforming standard stateless baselines. Our findings confirm that server-side adaptivity is a structural prerequisite for recovering clinical utility under realistic privacy budgets, providing a validated engineering blueprint for secure multi-institutional collaboration.

A Robust Framework for Secure Cardiovascular Risk Prediction: An Architectural Case Study of Differentially Private Federated Learning

Abstract

Accurate cardiovascular risk prediction is crucial for preventive healthcare; however, the development of robust Artificial Intelligence (AI) models is hindered by the fragmentation of clinical data across institutions due to stringent privacy regulations. This paper presents a comprehensive architectural case study validating the engineering robustness of FedCVR, a privacy-preserving Federated Learning framework applied to heterogeneous clinical networks. Rather than proposing a new theoretical optimizer, this work focuses on a systems engineering analysis to quantify the operational trade-offs of server-side adaptive optimization under utility-prioritized Differential Privacy (DP). By conducting a rigorous stress test in a high-fidelity synthetic environment calibrated against real-world datasets (Framingham, Cleveland), we systematically evaluate the system's resilience to statistical noise. The validation results demonstrate that integrating server-side momentum as a temporal denoiser allows the architecture to achieve a stable F1-score of 0.84 and an Area Under the Curve (AUC) of 0.96, statistically outperforming standard stateless baselines. Our findings confirm that server-side adaptivity is a structural prerequisite for recovering clinical utility under realistic privacy budgets, providing a validated engineering blueprint for secure multi-institutional collaboration.
Paper Structure (39 sections, 4 equations, 8 figures, 8 tables, 3 algorithms)

This paper contains 39 sections, 4 equations, 8 figures, 8 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Background Study of Federated Learning
  4. Federated Learning Process
  5. Distinguishing FedCVR from State-of-the-Art Baselines
  6. Versus Stateless Aggregators (FedAvg and FedProx)
  7. Versus Clustering Strategies (FedCluster)
  8. Versus Other Adaptive Optimizers (FedAdagrad and FedYogi)
  9. The FedCVR Advantage: Stateful Denoising
  10. FedCVR Aggregation Model
  11. System Orchestration and Privacy Preservation
  12. Privacy Preservation in Federated Learning
  13. Differential Privacy Implementation and Budgeting
  14. Privacy Budget Accounting (RDP)
  15. Justification of the Utility-Prioritized Regime
  16. ...and 24 more sections

Figures (8)

  • Figure 1: System Architecture of the FedCVR Framework. The diagram illustrates the cyclic federated process: The central server broadcasts the global model ($w_t$) to participating hospitals (Clients). Each client trains locally on private data and applies a Differential Privacy (DP) noise mechanism before uploading the protected gradients ($\tilde{g}_t$) for adaptive aggregation.
  • Figure 2: Schematic of the Horizontal Federated Learning (HFL) partitioning scheme. The global clinical dataset is distributed across distinct clients (e.g., hospitals), with each client sharing the same feature space (columns) but holding a disjoint subset of patient records (rows). This partition strategy simulates a realistic Non-IID multi-institutional environment where data structure is consistent but local distributions vary.
  • Figure 3: Frequency Distribution of Numerical Features. Histograms with Kernel Density Estimation (KDE) overlays showing the statistical distribution of Age, Systolic BP, Diastolic BP, and Cholesterol in the patient cohort. The distributions indicate a representative sample suitable for unbiased model training.
  • Figure 4: Statistical Distribution of Numerical Features. Box plots summarizing the central tendency and dispersion of Age, Blood Pressure (Systolic/Diastolic), and Cholesterol. The independent scales reveal the specific variance and outlier profile of each clinical marker in the synthetic dataset.
  • Figure 5: Evolution of Global Model Performance Metrics using FedCVR. The chart tracks key classification metrics (Accuracy, Precision, Recall, F1-Score) and Training Loss over 100 communication rounds. The rapid convergence of Loss (dotted gray) and stabilization of F1-Score (solid purple) demonstrate the efficiency of the adaptive optimization strategy.
  • ...and 3 more figures