Secure Federated Learning System for Healthcare Data Management with Privacy Preservation

ChangeBridge: Patent Apps - AI & Computing (G06N)

Published March 26th, 2026

Detected March 31st, 2026

Summary

USPTO published patent application US20260087167A1 for a secure federated learning system enabling healthcare institutions to train machine learning models locally on sensitive data without transferring raw information. The system incorporates AES and RSA encryption, secure aggregation, differential privacy protocols, and automated HIPAA and GDPR compliance monitoring. Filing date was February 14, 2025.

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What changed

USPTO published a patent application for a federated learning system designed for healthcare data management with privacy preservation and regulatory compliance capabilities. The invention enables multiple healthcare institutions to collaboratively train machine learning models without sharing raw patient data, using cryptographic techniques including AES and RSA encryption for model updates, secure aggregation protocols, and differential privacy mechanisms. The system also incorporates automated real-time compliance monitoring for HIPAA and GDPR adherence with logging and corrective action features.

This is a patent application publication rather than a regulatory requirement. No compliance deadline, required actions, or penalties apply. Healthcare institutions, medical device manufacturers, and pharmaceutical companies engaged in collaborative AI/ML model development may benefit from reviewing this innovation for potential licensing or implementation considerations. The modular architecture supports integration with existing healthcare IT infrastructure and flexible deployment options.

Source document (simplified)

← USPTO Patent Applications

SECURE FEDERATED LEARNING SYSTEM FOR HEALTHCARE DATA MANAGEMENT WITH PRIVACY PRESERVATION AND REGULATORY COMPLIANCE

Application US20260087167A1 Kind: A1 Mar 26, 2026

Inventors

Sabira Arefin

Abstract

The invention introduces a system and method for secure healthcare data management using federated learning, advanced encryption, and compliance monitoring. The system enables healthcare institutions to train machine learning models locally on sensitive data without transferring raw information, ensuring privacy and regulatory compliance. Model updates are encrypted using robust cryptographic techniques, such as AES and RSA, and transmitted securely to a central aggregator. Privacy-preserving protocols, including secure aggregation and differential privacy, ensure confidentiality during the creation of a global model that integrates insights from multiple institutions. The global model is validated locally, ensuring contextual relevance and continuous improvement. The system also incorporates real-time compliance monitoring to automate adherence to standards like HIPAA and GDPR, with detailed logging and corrective actions. Modular architecture supports seamless integration with existing infrastructures and flexible deployment options. This invention offers a scalable, secure, and privacy-preserving framework tailored to the complex demands of healthcare data security.