The DOI matches its Crossref record; 49 of the 50 references it cites were found in a registry or an index.
FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring
Open accessAbstract
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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“Tested on the human activity recognition dataset, FedHome with the generative convolutional autoencoder at both the cloud and edges, can outperform several benchmarks in terms of training accuracy and model size.”
From Federated learning for smart healthcare: A survey (2023) · cites this paper as [72]
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Cites
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1 of the 50 references couldn’t be matched to a known paper.
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Towards Personalized Federated Learning
Tan, A. Z., Yu, H., Cui, L., & Yang, Q. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3160699 · Cited by 1,474
Federated learning for smart healthcare: A survey
Nguyen, D. C., Pham, Q.-V., Pathirana, P. N., Ding, M., Seneviratne, A., Lin, Z., Dobre, O., & Hwang, W.-J. (2023) · ACM Computing Surveys · doi:10.1145/3501296 · Cited by 827
Federated learning for healthcare: Systematic review and architecture proposal
Antunes, R. S., André da Costa, C., Küderle, A., Yari, I. A., & Eskofier, B. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3501813 · Cited by 595
Remote patient monitoring using artificial intelligence: Current state, applications, and challenges
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Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey
Duan, S., Wang, D., Ren, J., Lyu, F., Zhang, Y., Wu, H., & Shen, X. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3218527 · Cited by 439
Metadata
- Authors
- Wu, Q., Chen, X., Zhou, Z., & Zhang, J.
- Affiliations
- Sun Yat-sen University (CN)
- Arizona State University (US)
- Venue
- IEEE Transactions on Mobile Computing
- Published
- 2022-08-01
- Volume
- 21
- Issue
- 8
- Pages
- 2818-2832
- ISSN
- 1536-1233 (ISSN-L)
- 1558-0660
- 2161-9875
- 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
- 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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