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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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IoT Federated Blockchain Learning at the Edge.

James Calo, Benny Lo

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

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Area of Science:

Medical Informatics
Distributed Systems
Artificial Intelligence

Background:

Internet of Things (IoT) devices are underutilized in medicine despite their benefits.
Current cloud-based architectures for medical machine learning face privacy and efficiency challenges.

Purpose of the Study:

To propose a decentralized federated learning framework for Internet of Medical Things (IoMT) devices.
To enhance privacy, efficiency, and collaborative model training at the edge for medical applications.

Main Methods:

Developed a distributed federated learning framework using blockchain for IoMT devices.
Implemented three paradigms: collaborative neural network training on IoT devices, private IoMT system training, and distributed network training distribution.

Main Results:

Enables collaborative model training while decoupling learning from sensitive datasets, ensuring privacy.
Facilitates private training of IoMT systems, crucial for confidential medical data.
Allows hospitals to utilize spare computing resources for distributed network model training.

Conclusions:

The proposed framework offers a decentralized, privacy-preserving, and efficient alternative to centralized cloud architectures for IoMT.
This approach supports dynamic adaptation and training on real-world data, advancing machine learning in medicine.