Balancing privacy and performance in healthcare: A federated learning framework for sensitive data

To design and evaluate a privacy-preserving federated learning (PPFL) framework for sensitive healthcare data, balancing robust privacy, model performance, and computational efficiency, while promoting user trust.

We integrated differentially private stochastic gradient descent (DPSGD) into a federated learning (FL) pipeline and evaluated the system on the Stroke Prediction Dataset. Experiments measured model utility (accuracy, F1), privacy ( ε ), resource usage, and trust features, with results compared to recent baselines.

The proposed framework achieved 93% accuracy on stroke risk prediction while maintaining a final privacy budget of ε 0.69 and minimal computational overhead. Our approach outperformed existing methods in privacy-utility trade-off, provided real-time privacy feedback, and is compliant with TRIPOD-AI/CLAIM recommendations.

This PPFL framework enables effective, trustworthy privacy-preserving ML in healthcare and resource-constrained settings. Future work will extend model architectures, regulatory alignment, and direct user trust assessment.