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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FedHome with a generative convolutional autoencoder at both the cloud and edges outperforms several benchmarks in terms of training accuracy and model size on the human activity recognition dataset.
“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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Self-citation
- 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 · 2021: 16
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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