International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 02 | Feb 2025
p-ISSN: 2395-0072
www.irjet.net
Privacy-Preserving Collaborative Learning for Healthcare Data Karthik Kamarapu1, Kali Rama Krishna Vucha2 1Independent Software Researcher, Osmania University, Hyderabad, TG, India.
2Independent Software Researcher, Acharya Nagarjuna University, Guntur, AP, India
---------------------------------------------------------------------***--------------------------------------------------------------------health care applications which integrates advanced Abstract - The integration of machine learning in health care has great benefits including improved patient outcomes, early disease detection and efficient resource management. However, strict privacy regulations and the decentralized nature of the healthcare present significant challenges to centralized model training. This research proposes a novel Privacy-Preserving Collaborative Learning Framework that leverages Federated Learning (FL) for decentralized training of machine learning models across healthcare institutions and Advanced privacy-preserving techniques such as differential privacy and homomorphic encryption ensure data confidentiality during the collaboration process. This framework also incorporates Explainable AI(XAI) tools to provide interpretability to clinicians and administrators. Experimental evaluations on benchmark datasets demonstrate the framework’s high predictive accuracy while addressing data heterogeneity, scalability and adversarial robustness.
privacy-preserving mechanisms such as differential privacy and homomorphic encryption for securing model updates during transmission. Explainable AI(XAI) techniques such as SHAP (SHapley Additive exPlanations) are incorporated to provide actionable insights and improve model interpretability. [7] Adaptive aggregation methods address data heterogeneity and ensures robust model performance across non-IID data distributions. The framework is designed to operate efficiently at scale supporting numerous institutions with minimal communication overhead. [8]
Key Words: Federated Learning, Privacy Preservation, Healthcare Data, Explainable AI, Data Collaboration
2. PROPOSED FRAMEWORK
Experimental evaluations using benchmark datasets including MIMIC-III and PhysioNet demonstrate the framework’s efficiency in real-world scenarios. Key results include high predictive accuracy, reduced privacy risks and enhanced model interpretability.
2. 1 Federated Learning Architecture
1.INTRODUCTION
The Federated Learning Architecture introduces a decentralized approach to training machine learning models that enables individual entities to build a global model in collaboration with each other while keeping the sensitive data locally. In the health care domain, this architecture is addressing privacy concerns by ensuring data security and complying with regulations like HIPAA and GDPR and by leveraging FL, health care institutions can share knowledge without compromising patient confidentiality. This section will delve into the details of this architecture.
The rapid adoption of digital health records and IoTenabled medical devices has created opportunities for improving clinical decision-making and operational efficiency. Machine learning models has proven effective in tasks such as disease prediction and resource optimization. However, privacy concerns and stringent regulations like HIPAA and GDPR restrict centralized data aggregation, creating significant barriers for traditional ML approaches in healthcare. [1], [2] Federated Learning has emerged has promising alternative that enables decentralized model training allowing institutions to collaboratively learn without sharing raw data. [3], [4] However, some challenges remain such as data is often heterogeneous with variations in distribution, quality and representation across institutions. Additionally, lack of interpretability in many FL-based models hinders clinical adoption as decision makers require explanations for predictions to ensure trust. [5] Furthermore, malicious participants injecting compromised updates can danger the integrity of the global model. [6]
Local Training Nodes: Each health care institution participating in the federated learning process acts as a local node. Within these nodes, the data never leaves premises ensuring privacy and regulatory compliance. The process begins with data pre-processing where features such as patient demographics, laboratory results and physiological signals are normalized and encoded to prepare them for model training. Min-Max Normalization: Used for scaling continuous features, such as age or laboratory test results to a specific range (e.g., [0, 1]).
To address these challenges, this research proposes a Privacy-Preserving Collaborative Learning Framework for
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