FedOSS : Federated Open Set Recognition via Inter-client Discrepancy and Collaboration

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

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Detail(s)

Original languageEnglish
Pages (from-to)190-202
Number of pages13
Journal / PublicationIEEE Transactions on Medical Imaging
Volume43
Issue number1
Online published10 Jul 2023
Publication statusPublished - Jan 2024

Abstract

Open set recognition (OSR) aims to accurately classify known diseases and recognize unseen diseases as the unknown class in medical scenarios. However, in existing OSR approaches, gathering data from distributed sites to construct large-scale centralized training datasets usually leads to high privacy and security risk, which could be alleviated elegantly via the popular cross-site training paradigm, federated learning (FL). To this end, we represent the first effort to formulate federated open set recognition (FedOSR), and meanwhile propose a novel Federated Open Set Synthesis (FedOSS) framework to address the core challenge of FedOSR: the unavailability of unknown samples for all anticipated clients during the training phase. The proposed FedOSS framework mainly leverages two modules, i.e., Discrete Unknown Sample Synthesis (DUSS) and Federated Open Space Sampling (FOSS), to generate virtual unknown samples for learning decision boundaries between known and unknown classes. Specifically, DUSS exploits inter-client knowledge inconsistency to recognize known samples near decision boundaries and then pushes them beyond decision boundaries to synthesize discrete virtual unknown samples. FOSS unites these generated unknown samples from different clients to estimate the class-conditional distributions of open data space near decision boundaries and further samples open data, thereby improving the diversity of virtual unknown samples. Additionally, we conduct comprehensive ablation experiments to verify the effectiveness of DUSS and FOSS. FedOSS shows superior performance on public medical datasets in comparison with state-of-the-art approaches. The source code is available at https://github.com/CityU-AIM-Group/FedOSS. © 2023 IEEE.

Research Area(s)

  • Biomedical imaging, Data models, Diseases, Federated Learning, Medical Image Classification, Open data, Open Set Recognition, Servers, Task analysis, Training

Bibliographic Note

Research Unit(s) information for this publication is provided by the author(s) concerned.