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The study aims to develop a federated framework for secure and efficient threat intelligence in remote patient monitoring systems.

February 6, 20260 citationsOpen Access

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Federated Temporal RAG for Privacy-Preserving Real-Time Threat Intelligence in Distributed Remote Patient Monitoring

AZAlam Mohammad ZahangirUniversity of Turku AEAyath Elhan

Key Points

The study aims to develop a federated framework for secure and efficient threat intelligence in remote patient monitoring systems.
Developed FedTempRAG framework for threat detection without sharing data.
Utilized decentralized retrieval and temporal knowledge graphs.
Implemented secure aggregation techniques for data privacy.
Achieved high detection accuracy in a multi-hospital IoMT environment.
Demonstrated low latency in threat detection processes.
Showed strong resilience against adversarial attacks.

Abstract

This paper presents FedTempRAG, a federated and privacy-preserving framework for real-time threat intelligence in distributed Remote Patient Monitoring (RPM) systems. By integrating decentralized retrieval, temporal knowledge graphs, and secure aggregation, the proposed approach enables collaborative attack detection without sharing sensitive data. Experimental results on multi-hospital IoMT environments demonstrate high detection accuracy, low latency, and strong resilience against adversarial behavior, highlighting its effectiveness for secure and practical healthcare deployments.

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Federated Temporal RAG for Privacy-Preserving Real-Time Threat Intelligence in Distributed Remote Patient Monitoring

Key Points

Abstract

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