FedSTEM-ADL: A federated spatial-temporal episodic memory model for ADL prediction

Learning of Activities of Daily Living (ADLs) provides insights into an individual’s habits, lifestyle, and well-being. However, it is crucial to address data privacy concerns in practical situations when learning the ADL routines of individuals. In this paper, we introduce FedSTEM-ADL, a federated...

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Main Authors: WU, Doudou, PATERIA, Shubham, SUBAGDJA, Budhitama, TAN, Ah-hwee
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Language:English
Published: Institutional Knowledge at Singapore Management University 2024
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Online Access:https://ink.library.smu.edu.sg/sis_research/9311
https://ink.library.smu.edu.sg/context/sis_research/article/10311/viewcontent/FedSTEM_ADL___IJCNN_2024.pdf
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spelling sg-smu-ink.sis_research-103112024-10-17T06:51:44Z FedSTEM-ADL: A federated spatial-temporal episodic memory model for ADL prediction WU, Doudou PATERIA, Shubham SUBAGDJA, Budhitama TAN, Ah-hwee Learning of Activities of Daily Living (ADLs) provides insights into an individual’s habits, lifestyle, and well-being. However, it is crucial to address data privacy concerns in practical situations when learning the ADL routines of individuals. In this paper, we introduce FedSTEM-ADL, a federated spatial-temporal episodic memory model to address this privacy issue. FedSTEM-ADL utilizes a federation of Spatial-Temporal Episodic Memory for ADLs (STEM-ADL) for federated learning, wherein multiple local STEM-ADL models from individual users are combined into a global model while preserving the privacy of the original data. Specifically, each local model is designed to learn the spatio-temporal ADL routines of an individual user, representing them as ADL events and sequences of such events as episode patterns. The global model then integrates the local models without referring to the underlying individual data, thus addressing privacy concerns in multi-user ADL analysis. We conduct a series of experiments based on both pseudo and real-world multi-user ADL datasets. The results show that FedSTEM-ADL is able to learn global ADL models in an efficient manner and consistently outperforms the baseline models in the task of next ADL event prediction. 2024-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/9311 info:doi/10.1109/IJCNN60899.2024.10650422 https://ink.library.smu.edu.sg/context/sis_research/article/10311/viewcontent/FedSTEM_ADL___IJCNN_2024.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Federated Learning Activities of Daily Living (ADLs) Self-Organizing Model Fusion ART Next Event Prediction Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Federated Learning
Activities of Daily Living (ADLs)
Self-Organizing Model
Fusion ART
Next Event Prediction
Databases and Information Systems
spellingShingle Federated Learning
Activities of Daily Living (ADLs)
Self-Organizing Model
Fusion ART
Next Event Prediction
Databases and Information Systems
WU, Doudou
PATERIA, Shubham
SUBAGDJA, Budhitama
TAN, Ah-hwee
FedSTEM-ADL: A federated spatial-temporal episodic memory model for ADL prediction
description Learning of Activities of Daily Living (ADLs) provides insights into an individual’s habits, lifestyle, and well-being. However, it is crucial to address data privacy concerns in practical situations when learning the ADL routines of individuals. In this paper, we introduce FedSTEM-ADL, a federated spatial-temporal episodic memory model to address this privacy issue. FedSTEM-ADL utilizes a federation of Spatial-Temporal Episodic Memory for ADLs (STEM-ADL) for federated learning, wherein multiple local STEM-ADL models from individual users are combined into a global model while preserving the privacy of the original data. Specifically, each local model is designed to learn the spatio-temporal ADL routines of an individual user, representing them as ADL events and sequences of such events as episode patterns. The global model then integrates the local models without referring to the underlying individual data, thus addressing privacy concerns in multi-user ADL analysis. We conduct a series of experiments based on both pseudo and real-world multi-user ADL datasets. The results show that FedSTEM-ADL is able to learn global ADL models in an efficient manner and consistently outperforms the baseline models in the task of next ADL event prediction.
format text
author WU, Doudou
PATERIA, Shubham
SUBAGDJA, Budhitama
TAN, Ah-hwee
author_facet WU, Doudou
PATERIA, Shubham
SUBAGDJA, Budhitama
TAN, Ah-hwee
author_sort WU, Doudou
title FedSTEM-ADL: A federated spatial-temporal episodic memory model for ADL prediction
title_short FedSTEM-ADL: A federated spatial-temporal episodic memory model for ADL prediction
title_full FedSTEM-ADL: A federated spatial-temporal episodic memory model for ADL prediction
title_fullStr FedSTEM-ADL: A federated spatial-temporal episodic memory model for ADL prediction
title_full_unstemmed FedSTEM-ADL: A federated spatial-temporal episodic memory model for ADL prediction
title_sort fedstem-adl: a federated spatial-temporal episodic memory model for adl prediction
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/sis_research/9311
https://ink.library.smu.edu.sg/context/sis_research/article/10311/viewcontent/FedSTEM_ADL___IJCNN_2024.pdf
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