FedFDD: Federated Learning with Frequency Domain Decomposition for Low-Dose CT Denoising

Xuhang Chen, Zeju Li, Zikun Xu, kaijie Xu, Cheng Ouyang, Chen Qin

Keywords: Federated Learning, Low-Dose CT Denoising, Discrete Cosine Transform.

Abstract:

Low-dose computed tomography (LDCT) enables imaging with minimal radiation exposure but typically results in noisy outputs. Deep learning algorithms have been emerging as popular tools for denoising LDCT images, where they typically rely on large data sets requiring data from multiple centers. However, LDCT images collected from different centers (clients) can present significant data heterogeneity, and the sharing of them between clients is also constrained by privacy regulations. In this work, we propose a personalized federated learning (FL) approach for enhancing model generalization across different organ images from multiple local clients while preserving data privacy. Empirically, we find that earlier FL methods tend to underperform single-set models on non-IID LDCT data due to the presence of data heterogeneity characterized by varying frequency patterns. To address this, we introduce a Federated Learning with Frequency Domain Decomposition (FedFDD) approach, which decomposes images into different frequency components and then updates high-frequency signals in an FL setting while preserving local low-frequency characteristics. Specifically, we leverage an adaptive frequency mask with discrete cosine transformation for the frequency domain decomposition. The proposed algorithm is evaluated on LDCT datasets of different organs and our experimental results show that FedFDD can surpass state-of-the-art FL methods as well as both localized and centralized models, especially on challenging LDCT denoising cases.
Our code is available at https://github.com/xuhang2019/FedFDD.

Submission Number: 238