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FedStack: Personalized activity monitoring using stacked federated learning by Thanveer Shaik; Xiaohui Tao; Niall Higgins; Raj Gururajan; Yuefeng Li; Xujuan Zhou; U. Rajendra Acharya is a Computer Science article available to read on EtoBox.

What is FedStack: Personalized activity monitoring using stacked federated learning about?

Recent advances in remote patient monitoring (RPM) systems can recognize various human activities to measure vital signs, including subtle motions from superficial vessels. There is a growing interest in applying artificial intelligence (AI) to this area of healthcare by addressing known limitations and challenges such as predicting and classifying vital signs and physical movements, which are considered crucial tasks. Federated learning is a relatively new AI technique designed to enhance data privacy by decentralizing traditional machine learning modeling. However, traditional federated learning requires identical architectural models to be trained across the local clients and global servers. This limits global model architecture due to the lack of local models’ heterogeneity. To overcome this, a novel federated learning architecture, FedStack, which supports ensembling heterogeneous architectural client models was proposed in this study. This work offers a protected privacy system for hospitalized in-patients in a decentralized approach and identifies optimum sensor placement. The proposed architecture was applied to a mobile health sensor benchmark dataset from 10 different sub

Who reads FedStack: Personalized activity monitoring using stacked federated learning?

Author

Thanveer Shaik; Xiaohui Tao; Niall Higgins; Raj Gururajan; Yuefeng Li; Xujuan Zhou; U. Rajendra Acharya

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)