Enhancing Privacy Preservation and Reducing Analysis Time with Federated Transfer Learning in Digital Twins-based Computed Tomography Scan Analysis

2025-09-09

AI Summary

Digital twin-based CT image analysis faces challenges including data privacy sensitivity, limited edge-device computational resources, and slow convergence and low accuracy due to non-independent and identically distributed (Non-IID) data across heterogeneous devices. To address these issues, this paper proposes a Digital Twin Federated Transfer Learning (DT-FTL) framework that synergistically integrates federated learning and transfer learning within a cloud-edge collaborative architecture. DT-FTL employs pre-trained model initialization, inter-node knowledge transfer, and lightweight local updates—ensuring patient raw data remains on-device and identity information is never exposed. Experiments on multi-center heterogeneous CT datasets demonstrate that DT-FTL achieves average improvements of 3.2–5.7% in accuracy, precision, recall, and F1-score over conventional federated learning and clustered federated approaches. It significantly enhances model generalization and real-time collaborative performance under Non-IID conditions, establishing a new paradigm for privacy-preserving, efficient, and robust intelligent medical image analysis.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Computer Vision: Multi-modal Vision
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

The application of Digital Twin (DT) technology and Federated Learning (FL) has great potential to change the field of biomedical image analysis, particularly for Computed Tomography (CT) scans. This paper presents Federated Transfer Learning (FTL) as a new Digital Twin-based CT scan analysis paradigm. FTL uses pre-trained models and knowledge transfer between peer nodes to solve problems such as data privacy, limited computing resources, and data heterogeneity. The proposed framework allows real-time collaboration between cloud servers and Digital Twin-enabled CT scanners while protecting patient identity. We apply the FTL method to a heterogeneous CT scan dataset and assess model performance using convergence time, model accuracy, precision, recall, F1 score, and confusion matrix. It has been shown to perform better than conventional FL and Clustered Federated Learning (CFL) methods with better precision, accuracy, recall, and F1-score. The technique is beneficial in settings where the data is not independently and identically distributed (non-IID), and it offers reliable, efficient, and secure solutions for medical diagnosis. These findings highlight the possibility of using FTL to improve decision-making in digital twin-based CT scan analysis, secure and efficient medical image analysis, promote privacy, and open new possibilities for applying precision medicine and smart healthcare systems.

Problem

• Enhancing privacy in CT scan analysis
• Reducing analysis time with federated learning
• Addressing data heterogeneity in medical imaging

Innovation

• Federated Transfer Learning for privacy protection
• Pre-trained models enable knowledge transfer
• Real-time cloud collaboration with Digital Twins