FedStack: Personalized Activity Monitoring using Stacked Federated Learning

Shaik, Thanveer and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Higgins, Niall and Gururajan, Raj ORCID: https://orcid.org/0000-0002-5919-0174 and Li, Yuefeng and Zhou, Xujuan and Acharya, U Rajendra (2022) FedStack: Personalized Activity Monitoring using Stacked Federated Learning. Knowledge-Based Systems, 257:109929. pp. 1-14. ISSN 0950-7051


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 subjects to classify 12 routine activities. Three AI models, artificial neural network (ANN), convolutional neural network (CNN), and bidirectional long short-term memory (Bi-LSTM) were trained on individual subject data. The federated learning architecture was applied to these models to build local and global models capable of state-of-the-art performances. The local CNN model outperformed ANN and Bi-LSTM models on each subject data. Our proposed work has demonstrated better performance for heterogeneous stacking of the local models compared to homogeneous stacking. Further analysis of the global heterogeneous CNN model determined that the optimum placement of the sensors on human limbs resulted in better activity recognition. This work sets the stage to build an enhanced RPM system that incorporates client privacy to assist with clinical observations for patients in an acute mental health facility and ultimately help to prevent unexpected death.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -)
Date Deposited: 27 Sep 2022 05:11
Last Modified: 02 Nov 2022 04:20
Uncontrolled Keywords: Federated Learning, ANN, CNN, Bi-LSTM, RPM, HAR
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4606 Distributed computing and systems software > 460605 Distributed systems and algorithms
46 INFORMATION AND COMPUTING SCIENCES > 4601 Applied computing > 460102 Applications in health
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning
Socio-Economic Objectives (2020): 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220408 Information systems
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence
Funding Details:
Identification Number or DOI: https://doi.org/10.1016/j.knosys.2022.109929
URI: http://eprints.usq.edu.au/id/eprint/51234

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