Privacy-Preserving Federated Learning with Domain Adaptation for Multi-Disease Ocular Disease Recognition
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 3219-3227 |
Journal / Publication | IEEE Journal of Biomedical and Health Informatics |
Volume | 28 |
Issue number | 6 |
Online published | 17 Aug 2023 |
Publication status | Published - Jun 2024 |
Link(s)
Abstract
As one of the effective ways of ocular disease recognition, early fundus screening can help patients avoid unrecoverable blindness. Although deep learning is powerful for image-based ocular disease recognition, the performance mainly benefits from a large number of labeled data. For ocular disease, data collection and annotation in a single site usually take a lot of time. If multi-site data are obtained, there are two main issues: 1) the data privacy is easy to be leaked; 2) the domain gap among sites will influence the recognition performance. Inspired by the above, first, a Gaussian randomized mechanism is adopted in local sites, which are then engaged in a global model to preserve the data privacy of local sites and models. Second, to bridge the domain gap among different sites, a two-step domain adaptation method is introduced, which consists of a domain confusion module and a multi-expert learning strategy. Based on the above, a privacy-preserving federated learning framework with domain adaptation is constructed. In the experimental part, a multi-disease early fundus screening dataset, including a detailed ablation study and four experimental settings, is used to show the stepwise performance, which verifies the efficiency of our proposed framework. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
Research Area(s)
- Adaptation models, Data models, Data privacy, data privacy, Diseases, domain adaptation, Federated learning, federated learning, Image recognition, ocular disease, Training
Citation Format(s)
Privacy-Preserving Federated Learning with Domain Adaptation for Multi-Disease Ocular Disease Recognition. / Tang, Zhiri; Wong, Hau-San; Yu, Zekuan.
In: IEEE Journal of Biomedical and Health Informatics, Vol. 28, No. 6, 06.2024, p. 3219-3227.
In: IEEE Journal of Biomedical and Health Informatics, Vol. 28, No. 6, 06.2024, p. 3219-3227.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review