DECENTRALIZED GOVERNANCE MODEL FOR DATA DISSEMINATION DURING FEDERATED LEARNING FOR HEALTHCARE

Abstract

The proposed invention presents a decentralized federated learning system for secure and efficient patient data management in healthcare. By leveraging blockchain technology, advanced encryption, and decentralized storage, the system ensures patient privacy while facilitating real-time data sharing among healthcare providers. This innovative approach allows for collaborative diagnostics, enhances interoperability between various healthcare platforms, and empowers patients by granting them control over their health information. Additionally, the incorporation of artificial intelligence enables predictive analytics for personalized medicine and proactive public health measures. The invention addresses regulatory compliance and cybersecurity, ultimately transforming healthcare practices to improve patient outcomes and foster a more integrated healthcare ecosystem.

Specification

Description:The proposed system falls within the field of medical data management and privacy-preserving technologies, specifically focusing on the integration of decentralized federated learning (DFL) in healthcare settings. This innovation addresses critical challenges related to the secure transfer of patient information among various healthcare entities, including doctors, laboratories, and medical institutions, while adhering to stringent regulations like GDPR and EHDS. By leveraging DFL, the system enables the development of accurate diagnostic models without compromising patient privacy, as only model parameters are shared rather than raw data.

Additionally, the incorporation of blockchain technology ensures data integrity and prevents unauthorized access, offering a tamper-proof record of medical information. Coupled with IPFS for decentralized storage of DICOM images and logistic map encryption for text data, this system enhances both the security and accessibility of medical records. Overall, the proposed solu , Claims:1. The invention utilizes decentralized federated learning (DFL) to enable healthcare institutions to collaboratively train diagnostic models while ensuring patient data remains local and private, thus maintaining compliance with regulations like GDPR.

2. The integration of blockchain technology provides an immutable and transparent ledger for patient data interactions, ensuring data integrity and accountability among all healthcare providers involved in patient care.

3. The system employs logistic map encryption to safeguard sensitive text data, ensuring that unauthorized access is effectively mitigated and maintaining the confidentiality of patient information.

4. By utilizing the InterPlanetary File System (IPFS) for storing and retrieving DICOM images, the invention enhances the efficiency of data exchange, allowing rapid access to critical medical imaging.

5. The user-friendly interface of the proposed system enables healthcare professionals to access and interpret complex data insights easily, promo

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