IoT Federated Blockchain Learning at the Edge

• URL: http://arxiv.org/abs/2304.03006v1
• Date: Thu, 6 Apr 2023 11:32:40 GMT
• Title: IoT Federated Blockchain Learning at the Edge
• Authors: James Calo and Benny Lo
• Abstract summary: We propose a distributed federated learning framework for IoT devices, more specifically for IoMT (Internet of Medical Things) We use blockchain to allow for a decentralized scheme improving privacy and efficiency over a centralized system. The system is designed for three paradigms: 1) Training neural networks on IoT devices to allow for collaborative training of a shared model whilst decoupling the learning from the dataset to ensure privacy.
• Score: 14.689706366051956
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: IoT devices are sorely underutilized in the medical field, especially within machine learning for medicine, yet they offer unrivaled benefits. IoT devices are low-cost, energy-efficient, small and intelligent devices. In this paper, we propose a distributed federated learning framework for IoT devices, more specifically for IoMT (Internet of Medical Things), using blockchain to allow for a decentralized scheme improving privacy and efficiency over a centralized system; this allows us to move from the cloud-based architectures, that are prevalent, to the edge. The system is designed for three paradigms: 1) Training neural networks on IoT devices to allow for collaborative training of a shared model whilst decoupling the learning from the dataset to ensure privacy. Training is performed in an online manner simultaneously amongst all participants, allowing for the training of actual data that may not have been present in a dataset collected in the traditional way and dynamically adapt the system whilst it is being trained. 2) Training of an IoMT system in a fully private manner such as to mitigate the issue with confidentiality of medical data and to build robust, and potentially bespoke, models where not much, if any, data exists. 3) Distribution of the actual network training, something federated learning itself does not do, to allow hospitals, for example, to utilize their spare computing resources to train network models.

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