Federated Learning with Differential Privacy for Healthcare Applications

Healthcare data is highly sensitive and subject to strict privacy regulations, making it challenging to develop machine learning models that can benefit from data across multiple institutions. Federated learning offers a promising solution by enabling collaborative model training without sharing raw data. However, existing federated learning approaches may still leak sensitive information through model updates.In this work, we present a comprehensive federated learning framework specifically designed for healthcare applications that incorporates differential privacy mechanisms to provide formal privacy guarantees. Our approach enables multiple healthcare institutions to collaboratively train diagnostic models while ensuring that individual patient information remains private.We demonstrate the effectiveness of our framework on three healthcare tasks: medical image classification, electronic health record analysis, and drug discovery. Our results show that federated learning with differential privacy can achieve diagnostic accuracy within 2% of centralized training while providing strong privacy guarantees.

Our federated learning framework consists of four main components: (1) Secure Aggregation Protocol that prevents the central server from accessing individual model updates, (2) Differential Privacy Mechanism that adds calibrated noise to model updates, (3) Adaptive Client Selection that optimizes participation based on data quality and privacy budget, and (4) Byzantine Fault Tolerance that handles malicious or faulty participants.

The differential privacy mechanism uses the Gaussian mechanism with privacy budget allocation across training rounds. We employ moment accountant techniques to track privacy loss and ensure that the total privacy budget is not exceeded. The adaptive client selection algorithm balances model performance with privacy constraints by selecting participants that maximize utility while minimizing privacy cost.

We evaluated our framework on three healthcare datasets: (1) Chest X-ray classification with 10 hospitals, (2) Electronic health record analysis with 15 clinics, and (3) Drug discovery with 8 pharmaceutical companies. The federated models achieved 94.2% accuracy on chest X-ray classification (vs. 96.1% centralized), 89.7% on EHR analysis (vs. 91.3% centralized), and identified 87% of known drug-target interactions (vs. 92% centralized).

Privacy analysis showed that our framework provides (ε=1.0, δ=10^-5)-differential privacy while maintaining high utility. Communication costs were reduced by 90% compared to naive federated learning through our adaptive client selection and model compression techniques.