Privacy-Preserving Federated Learning with Domain Adaptation for Multi-Disease Ocular Disease Recognition

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

5 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)3219-3227
Journal / PublicationIEEE Journal of Biomedical and Health Informatics
Volume28
Issue number6
Online published17 Aug 2023
Publication statusPublished - Jun 2024

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