Federated Deep Learning for Next-Generation Infusion Pump Systems

Abstract

Delivering precise drug dosages, particularly in critical care, requires infusion pumps. Traditional systems, however, have drawbacks like sluggish user interfaces, security flaws, little personalization, and privacy issues. This project uses a federated deep learning framework to present an advanced infusion pump system. Autoencoders identify abnormalities in patient or device behavior, improving safety and early fault detection, while LSTM networks process real-time physiological data to dynamically modify infusion. By optimizing dosage through ongoing patient-specific feedback and learning, reinforcement learning further enhances care. Federated learning allows decentralized training across several devices without transferring sensitive patient data, addressing privacy concerns and guaranteeing compliance with FDA, GDPR, and HIPAA regulations.The system can adjust to individual needs while preserving high security and data integrity thanks to the integration of these technologies. The system, which is made for both home-care and hospital settings, guarantees user trust, scalability, and dependability. In the end, it provides a solid basis for patient-centered, compliant, and intelligent infusion therapy that develops in response to new clinical data.

Introduction

Infusion pumps are vital medical devices for delivering precise drug dosages, especially in critical care and home therapy. Traditional pumps face issues like slow user interfaces and vulnerabilities to data privacy and cybersecurity threats. To address these, the project proposes an intelligent, adaptive infusion pump system using advanced machine learning techniques.

The system integrates multiple technologies:

• Autoencoders detect anomalies in device behavior or patient responses.

• LSTM networks analyze real-time physiological data to adjust infusion rates dynamically.

• Reinforcement Learning optimizes dosing based on ongoing patient feedback.

• Federated Learning allows decentralized model training on local devices, preserving patient privacy and complying with regulations like HIPAA, GDPR, and FDA standards.

Prior research in related areas has explored anomaly detection, federated learning, hybrid machine learning models, and decentralized approaches, each with benefits and limitations concerning accuracy, privacy, and deployment complexity.

Existing methods often use CNNs combined with traditional classifiers to detect infusion pump anomalies but struggle with long-term dependencies and raise privacy concerns due to centralized data.

The proposed federated deep learning approach improves on this by enabling real-time, patient-specific dosage adjustments and anomaly detection while maintaining privacy through decentralized training.

Experimental results show that this integrated model achieves high accuracy (up to 93% training, 89% validation), better generalization, and more reliable anomaly detection compared to traditional CNN-based methods, with less overfitting.

Overall, the project demonstrates a scalable, privacy-conscious, and clinically effective next-generation infusion pump system that enhances safety and personalized care through cutting-edge AI technologies.

Conclusion

This project uses a federated deep learning framework to present a sophisticated solution for next-generation infusion pumps. The system efficiently learns from decentralized medical data while maintaining privacy by fusing LSTM, autoencoders, federated learning, and reinforcement learning. The model is appropriate for real-time, vital healthcare applications due to its high accuracy and strong generalization. Its capacity for constant learning and adaptation guarantees more intelligent and secure infusion management. This strategy represents a major advancement in the development of morally and intelligent AI-powered medical equipment.

References

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Copyright

Copyright © 2025 Rajani Kumar Sindavalam, Nagaraju Dodda. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.