Cited by 392Privacy-Preserving Technologies in DataIoT and Edge/Fog ComputingInternet of Things and AI

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FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring

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Wu, Qiong, Chen, Xu, Zhou, Zhi, Zhang, Junshan·2022·IEEE Transactions on Mobile Computing

DOI 10.1109/tmc.2020.3045266

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Abstract

In-home health monitoring has attracted great attention for the ageing population worldwide. With the abundant user health data accessed by Internet of Things (IoT) devices and recent development in machine learning, smart healthcare has seen many successful stories. However, existing approaches for in-home health monitoring do not pay sufficient attention to user data privacy and thus are far from being ready for large-scale practical deployment. In this paper, we propose FedHome, a novel cloud-edge based federated learning framework for in-home health monitoring, which learns a shared global model in the cloud from multiple homes at the network edges and achieves data privacy protection by keeping user data locally. To cope with the imbalanced and non-IID distribution inherent in user’s monitoring data, we design a generative convolutional autoencoder (GCAE), which aims to achieve accurate and personalized health monitoring by refining the model with a generated class-balanced dataset from user’s personal data. Besides, GCAE is lightweight to transfer between the cloud and edges, which is useful to reduce the communication cost of federated learning in FedHome. Extensive experiments based on realistic human activity recognition data traces corroborate that FedHome significantly outperforms existing widely-adopted methods.

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Self-citation

Authors: Wu, Q., Chen, X., Zhou, Z., & Zhang, J.
Affiliations: Sun Yat-sen University (CN)
Arizona State University (US)
ORCID: Wu, Qiong
Chen, Xu
Zhou, Zhi
Zhang, Junshan
Venue: IEEE Transactions on Mobile Computing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2022-08-01
Volume: 21
Issue: 8
Pages: 2818-2832
ISSN: 1536-1233 (ISSN-L)
1558-0660
2161-9875
DOI: 10.1109/tmc.2020.3045266
OpenAlex: W3111512201
Cited by: 392
References: 50
Citations by year: 2026: 51 · 2025: 85 · 2024: 124 · 2023: 78 · 2022: 38
FWCI: 12.86
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Mobile Computing (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: National Key Research and Development Program of China (2017YFB1001703)
National Natural Science Foundation of China (U20A20159, 61972432)
Program for Guangdong Introducing Innovative and Entrepreneurial Teams (2017ZT07X355)
Guangdong Provincial Pearl River Talents Program (2017GC010465)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
Keywords: personalized federated learning, home health monitoring, cloud-edge collaboration, data privacy, class imbalance, non-IID data, human activity recognition, Internet of Things, communication cost reduction, smart healthcare

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FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring

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