Semi-centralized federated learning network for low-dose CT imaging

Danyang Li; Hao Wang; Jingyi Liao; Mingqiang Meng; Cuidie Zeng; Yuting Wang; Dong Zeng; Jianhua Ma

doi:10.1117/12.2654180

7 April 2023 Semi-centralized federated learning network for low-dose CT imaging

Danyang Li, Hao Wang, Jingyi Liao, Mingqiang Meng, Cuidie Zeng, Yuting Wang, Dong Zeng, Jianhua Ma

Proceedings Volume 12463, Medical Imaging 2023: Physics of Medical Imaging; 124634W (2023) https://doi.org/10.1117/12.2654180
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Federated learning method shows great potential in computed tomography imaging field by utilizing a decentralized strategy with data privacy-preserving for local medical institutions. However, directly aggregating the parameters of each local model would degrade the generalization performance of the updated global model. In addition, well paired centralized training datasets can be collected in real world, which are not included in the current federated learning methods. To address the issue, we present a semi-centralized federated learning method to promote the generalization performance of the learned global model. Specifically, each local model is firstly trained locally at a fixed round, then, the parameters are aggregated on server to initialized the global model. After that, the global model is further trained with a standard dataset on the server, which contains well paired training samples to stabilize and standardize the global model. For shorten, we call the presented semi-centralized federated learning method as “SC-FL”. Experimental results on different local datasets demonstrate the presented SC-FL outperforms the competing methods.

Citation Download Citation

Danyang Li, Hao Wang, Jingyi Liao, Mingqiang Meng, Cuidie Zeng, Yuting Wang, Dong Zeng, and Jianhua Ma "Semi-centralized federated learning network for low-dose CT imaging", Proc. SPIE 12463, Medical Imaging 2023: Physics of Medical Imaging, 124634W (7 April 2023); https://doi.org/10.1117/12.2654180

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