Accuracy Optimization of Lightweight Home Health Models via Fog Computing, Federated Learning and Source-Free Domain Adaptation
Main Article Content
Abstract
Aiming at the dual constraints of data silos and privacy protection in home health monitoring scenarios, a lightweight model accuracy optimization method integrating fog computing architecture, federated learning mechanism and source-free domain adaptation technology is proposed. The adaptive aggregation strategy operates on encrypted gradient parameters rather than raw feature statistics, and the distribution matching is performed on aggregated prototype vectors at the cloud server rather than individual client data, preventing lifestyle pattern leakage. An adaptive aggregation strategy is deployed on edge fog nodes to enable cross-domain knowledge transfer via feature alignment and distribution matching, which improves the generalization ability of target domain models without accessing source domain data. Experimental results showed that this method compressed the model parameters to 18.7% of the baseline model on multiple home health datasets, improved the cross-domain recognition accuracy by 12.4 percentage points, and reduced communication overhead by 63.2%, providing a feasible technical path for distributed intelligent health monitoring in resource-constrained environments. The distributed design is consistent with wireless home-health monitoring where physiological and activity data remain locally protected.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
A. Alanazi, S. Alahmari, and Y. Liu, “Indoor localization system: a deep learning approach using channel state information,” Wireless Networks, no. prepublish, pp. 1-13, 2026, doi: 10.1007/s11276-026-04124-4.
Z. Ezzahra F, A. Sana, Q. Sara, et al., “Multi-objective reinforcement learning for recommender systems: a comprehensive survey of methods, challenges, and future directions,” International Journal of Multimedia Information Retrieval, vol. 14, no. 4, pp. 33-33, 2025, doi: 10.1007/s13735-025-00383-7.
S. Quadri Q A, “Leveraging mass gathering events as experiential learning platform for healthcare professional education: Opportunities, challenges, and future directions,” Mass Gathering Medicine, vol. 4, Art. no. 100027, 2025, doi: 10.1016/j.mgmed.2025.100027.
Z. Belfeki, M. Krichen, M. Bouazizi, et al., “Federated learning for natural disaster management: challenges, opportunities, and future directions,” Cluster Computing, vol. 28, no. 10, pp. 650, 2025, doi: 10.1007/s10586-025-05353-6.
P. Verma, N. Bharot, J. Breslin G, et al., “Leveraging Transfer Learning Domain Adaptation Model With Federated Learning to Revolutionise Healthcare,” Expert Systems, vol. 42, no. 2, Art. no. e13827, 2024, doi: 10.1111/exsy.13827.
S. Abbas R, Z. Abbas, A. Zahir, et al., “Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration,” Healthcare, vol. 12, no. 24, pp. 2587, 2024, doi: 10.3390/healthcare12242587.
S. Rajagopal M and R. Buyya, “Leveraging blockchain and federated learning in Edge-Fog-Cloud computing environments for intelligent decision-making with ECG data in IoT,” Journal of Network and Computer Applications, vol. 233, Art. no. 104037, 2025, doi: 10.1016/j.jnca.2024.104037.
Y. Xu, M. Xiao J, C. Wu, et al., “Age-of-Information-Aware Federated Learning,” Journal of Computer Science and Technology, vol. 39, no. 3, pp. 637-653, 2024, doi: 10.1007/s11390-024-3914-x.
S. Tripathy S, S. Bebortta, C. Chowdhary L, et al., “FedHealthFog: A federated learning-enabled approach towards healthcare analytics over fog computing platform,” Heliyon, vol. 10, no. 5, Art. no. e26416, 2024, doi: 10.1016/j.heliyon.2024.e26416.
S. Jiang, Y. Li, F. Firouzi, et al., “Federated clustered multi-domain learning for health monitoring,” Scientific Reports, vol. 14, no. 1, pp. 903, 2024, doi: 10.1038/s41598-024-51344-9.
S. Qingshan, C. Tie, F. Feng, et al., “Improved Domain Adaptation Network Based on Wasserstein Distance for Motor Imagery EEG Classification,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023, doi: 10.1109/TNSRE.2023.3241846.
A. Mansoor, N. Faisal, T. Muhammad, et al., “Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey,” IEEE Journal of Biomedical and Health Informatics, 2022, doi: 10.1109/JBHI.2022.3181823.
D. Eshagh, E. Bernhard, T. Ballber N, et al., “Using Channel State Information for Physical Tamper Attack Detection in OFDM Systems: A Deep Learning Approach,” IEEE Wireless Communications Letters, vol. 10, no. 7, pp. 1503-1507, 2021, doi: 10.1109/LWC.2021.3072937.
X. Chendong, W. Weigang, Z. Yunwei, et al., “An Indoor Localization System Using Residual Learning with Channel State Information,” Entropy, vol. 23, no. 5, pp. 574, 2021, doi: 10.3390/e23050574.
J. Sanguk, L. Jaehee, and S. Jaewoo, “Deep learning-based massive multiple-input multiple-output channel state information feedback with data normalisation using clipping,” Electronics Letters, vol. 57, no. 3, pp. 151-154, 2021, doi: 10.1049/ell2.12080.