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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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 |
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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 |
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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. |
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author |
WU, Doudou PATERIA, Shubham SUBAGDJA, Budhitama TAN, Ah-hwee |
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WU, Doudou PATERIA, Shubham SUBAGDJA, Budhitama TAN, Ah-hwee |
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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 |
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Institutional Knowledge at Singapore Management University |
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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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