โก Quick Summary
This study presents a groundbreaking Federated Learning (FL) deployment for ECG-based Clinical Decision Support Systems (CDSS), demonstrating its effectiveness in real-world healthcare applications. The system achieved an impressive F1 score of 93%, showcasing competitive accuracy while enhancing data privacy and system scalability.
๐ Key Details
- ๐ Devices Used: Up to 8 heterogeneous IoT edge devices
- โ๏ธ Technology: Federated Learning for decentralized model training
- ๐ Performance: F1 score of 93% compared to centralized approach (97%)
- ๐ Privacy: Sensitive ECG data remains decentralized
๐ Key Takeaways
- ๐ Federated Learning enables collaborative model training without data centralization.
- ๐ก Real-world deployment bridges the gap between theory and practical application.
- ๐ฅ Clinical relevance demonstrated through ECG arrhythmia detection.
- ๐ Competitive performance achieved while enhancing data privacy.
- ๐ Scalability allows for broader implementation across diverse healthcare environments.
- ๐ค AI-driven CDSS can lead to personalized patient-centric healthcare.
- ๐ Study conducted by a team of researchers at IEEE Engineering in Medicine and Biology Society.
- ๐ PMID: 41337150.
๐ Background
The integration of Federated Learning within the Internet of Medical Things (IoMT) is a significant advancement in developing privacy-preserving Clinical Decision Support Systems. Traditional methods often require centralizing sensitive patient data, raising concerns about privacy and security. This study aims to address these challenges by implementing a decentralized approach that maintains patient confidentiality while still providing valuable insights for clinical decision-making.
๐๏ธ Study
Conducted in a laboratory setting, this study focused on deploying a Federated Learning framework for ECG arrhythmia detection. The researchers utilized a setup involving multiple IoT edge devices, allowing for collaborative training of machine learning models without the need to share sensitive ECG data. This innovative approach not only preserves patient privacy but also enhances the feasibility of implementing AI-driven solutions in real-world healthcare settings.
๐ Results
The performance evaluations revealed that the proposed framework achieved an F1 score of 93%, which, while slightly lower than the 97% score of a centralized approach, demonstrates the potential of Federated Learning to deliver competitive accuracy. This finding is particularly significant as it highlights the ability to maintain high performance levels while prioritizing data privacy and security.
๐ Impact and Implications
The implications of this study are profound. By successfully demonstrating the effectiveness of Federated Learning in a real-world context, the research paves the way for broader adoption of AI-driven Clinical Decision Support Systems across various healthcare environments. This approach not only enhances patient privacy but also supports the scalability of healthcare solutions, ultimately leading to more personalized and effective patient care.
๐ฎ Conclusion
This study highlights the transformative potential of Federated Learning in the realm of healthcare. By enabling decentralized training of AI models, we can achieve competitive performance while safeguarding patient data. The future of personalized patient-centric healthcare looks promising, and further research in this area is encouraged to explore the full capabilities of AI in clinical settings.
๐ฌ Your comments
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A Laboratory-Based Federated Learning Deployment on Real Devices for ECG-Based Clinical Decision Support Systems.
Abstract
Integrating Federated Learning (FL) in the Internet of Medical Things (IoMT) enables the development of privacy-preserving Clinical Decision Support Systems (CDSS) for real-world healthcare applications. However, most FL research has been confined to simulated environments. This work presents a fully deployed real-world FL deployment for CDSS, validated through an electrocardiogram (ECG) arrhythmia detection scenario running on heterogeneous Internet of Things (IoT) edge devices. Unlike simulated approaches, the proposed system implements an IoT setup that allows collaborative model training without sharing or centralizing sensitive ECG data. Performance evaluations on a maximum of eight devices with varying computational capabilities demonstrate the presented framework’s adaptability, achieving an F1 score of 93% compared to a centralized approach (97%). The results indicate that real-world FL deployment achieves competitive accuracy while significantly enhancing data privacy, system scalability, and practical feasibility. By bridging the gap between FL theory and real-world implementation, these findings validate the potential of FL for Artificial Intelligence (AI) driven CDSS for personalized patient-centric healthcare.Clinical relevance- This work demonstrates that AI models for early diagnosis and personalized treatment can be trained on decentralized patient data using a Federated Learning approach that preserves privacy, achieves competitive performance comparable to centralized methods, and enables scalable clinical decision support across heterogeneous healthcare environments.
Author: [‘Shumba AT’, ‘Cantoro D’, ‘Montanaro T’, ‘Semeraro G’, ‘Sergi I’, ‘De Vittorio M’, ‘Patrono L’]
Journal: Annu Int Conf IEEE Eng Med Biol Soc
Citation: Shumba AT, et al. A Laboratory-Based Federated Learning Deployment on Real Devices for ECG-Based Clinical Decision Support Systems. A Laboratory-Based Federated Learning Deployment on Real Devices for ECG-Based Clinical Decision Support Systems. 2025; 2025:1-6. doi: 10.1109/EMBC58623.2025.11254070