A Resource-Constrained and Privacy-Preserving Edge-Computing-Enabled Clinical Decision System: A Federated Reinforcement Learning Approach

Xue, Z; Zhou, P; Xu, Z; Wang, X; Xie, Y; Ding, X; Wen, S

A Resource-Constrained and Privacy-Preserving Edge-Computing-Enabled Clinical Decision System: A Federated Reinforcement Learning Approach

Xue, Z Zhou, P Xu, Z Wang, X Xie, Y Ding, X Wen, S

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2021, 8, (11), pp. 9122-9138
Issue Date:: 2021-06-01

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Field	Value	Language
dc.contributor.author	Xue, Z
dc.contributor.author	Zhou, P
dc.contributor.author	Xu, Z
dc.contributor.author	Wang, X
dc.contributor.author	Xie, Y
dc.contributor.author	Ding, X
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.date.accessioned	2021-09-12T05:35:22Z
dc.date.available	2021-09-12T05:35:22Z
dc.date.issued	2021-06-01
dc.identifier.citation	IEEE Internet of Things Journal, 2021, 8, (11), pp. 9122-9138
dc.identifier.issn	2327-4662
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/150483
dc.description.abstract	Internet-of-Things-enabled E-health system, which could monitor and collect the personal health information (PHI), has gradually transformed the clinical treatment to a more personalized way with in-home monitoring smart devices. Then, with the collected PHI, clinical decision support systems (CDSSs), which are based on data mining techniques and historical electronic medical records (EMRs) to help clinicians make proper treatment decisions, have attracted considerable attention. To address issues, such as network congestion and low rate of responsiveness for traditional methods when implementing CDSSs, we integrate the technologies mobile-edge computing (MEC) and software-defined networking for exploiting the computation resources and storage capacities among edge nodes (ENs) (i.e., MEC servers) in our model. Based on this integrated system, each edge node will deploy a double deep $Q$ -network (DDQN) to obtain a stable and sequential clinical treatment policy. It is enabled by a novel fully decentralized federated framework (FDFF) for aggregating models of DDQN and extracting the knowledge from EMRs across all ENs. Furthermore, we discuss the convergence of FDFF in resource-constrained environments. However, since most EMRs are faced with stringent privacy concerns, we adopt two additively homomorphic encryption schemes to prevent leakage of EMRs' privacy during the training process of FDFF. Finally, we measure the time cost of our additively homomorphic encryption schemes and validate DDQN with experiments on large data sets based on FDFF, which shows promising performance on clinician treatment.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2021.3057653
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.title	A Resource-Constrained and Privacy-Preserving Edge-Computing-Enabled Clinical Decision System: A Federated Reinforcement Learning Approach
dc.type	Journal Article
utslib.citation.volume	8
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2021-09-12T05:35:20Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	8
utslib.citation.issue	11

Abstract:

Internet-of-Things-enabled E-health system, which could monitor and collect the personal health information (PHI), has gradually transformed the clinical treatment to a more personalized way with in-home monitoring smart devices. Then, with the collected PHI, clinical decision support systems (CDSSs), which are based on data mining techniques and historical electronic medical records (EMRs) to help clinicians make proper treatment decisions, have attracted considerable attention. To address issues, such as network congestion and low rate of responsiveness for traditional methods when implementing CDSSs, we integrate the technologies mobile-edge computing (MEC) and software-defined networking for exploiting the computation resources and storage capacities among edge nodes (ENs) (i.e., MEC servers) in our model. Based on this integrated system, each edge node will deploy a double deep $Q$ -network (DDQN) to obtain a stable and sequential clinical treatment policy. It is enabled by a novel fully decentralized federated framework (FDFF) for aggregating models of DDQN and extracting the knowledge from EMRs across all ENs. Furthermore, we discuss the convergence of FDFF in resource-constrained environments. However, since most EMRs are faced with stringent privacy concerns, we adopt two additively homomorphic encryption schemes to prevent leakage of EMRs' privacy during the training process of FDFF. Finally, we measure the time cost of our additively homomorphic encryption schemes and validate DDQN with experiments on large data sets based on FDFF, which shows promising performance on clinician treatment.

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http://hdl.handle.net/10453/150483