# Privacy-Preserving Transfer Learning Framework for Kidney Disease Detection
## 1 Introduction  
Chronic kidney disease (CKD) is a widespread health issue that impacts over 10% of the global population and directly affects an incredible amount of 800 million people. The prevalence of CKD is notably higher in older people, women, racial minorities, and those with chronic conditions like as diabetes mellitus and hypertension. CKD is a prominent contributor to global mortality and is among the limited number of non-communicable diseases that have contributed to a rise in deaths during the past two years [1]. In the year 2016, the disease ranked as the 16 th most common cause of mortality. Projections indicate that by 2040, it will rank as one of the most lethal diseases, increasing to the fifth position [2]. The high number of affected individuals and the significant negative impact of CKD call for urgent solutions to develop early diagnosis systems [1]. Machine imaging techniques, including magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), mammography, ultrasound, and X-ray, are extensively employed in the medical imaging domain [3]. Recent developments in medical imaging methods have been crucial for the early diagnosis and treatment of diseases. Nevertheless, this procedure is vulnerable to human error as the task of visual interpretation is typically carried out by highly skilled professionals and these professionals are not always available. Therefore, automated systems are always needed to help doctors' diagnosis decisions. Although traditional machine learning approaches may not be adequate for properly and effectively analyzing the complexity of medical images, the combination of deep learning methods and the advancement of fast processors enables precise and efficient analysis of medical images. This approach enables the identification and interpretation of patterns in medical imaging. Historically, the medical sector has not completely harnessedartificial intelligence owing to hardware constraints. However, recent advancements in deep learning algorithms have seen significant progress in the healthcare domain. These advancements have resulted in a monumental revolution in the domain of medical image processing. Specifically, considering the constraints of the convolutional neural networks (CNNs) approach, deep learning models show significant promise in the healthcare sector. Utilizing this approach enables healthcare practitioners to examine complex patterns in medical images with higher precision, therefore offering more efficient and expedited solutions in the realms of diagnosis [4]. CNN is a highly efficient deep learning model in the field of image processing, primarily employed for image classification and recognition. A CNN is composed of several layers that divide the inputs into smaller regions, employing convolution filters. Customization of this model enables the identification and determination of significant characteristics in medical images. In the context of CT kidney imaging, a CNN model can provide significant assistance in the diagnosis of kidney diseases [5]. Radiological imaging is of utmost importance in the identification and assessment of kidney problems. These images provide a detailed depiction of the internal structure and operational characteristics of the kidneys. Nevertheless, the interpretation of these images is a laborious and specialized procedure. CNN models have the capability to identify anomalies in CT scans, therefore facilitating the early diagnosis of diseases. The implementation of this technology can facilitate the early diagnosis of diseases and enhance the management of treatment. The advancement and use of these technologies can serve as a crucial measure in addressing significant health issues, including CDK [6; 7]. On the other hand, studies emphasize the issue that disease data, such as CT scans, are highly sensitive. Rigid privacy measures are essential in this situation to protect patients' sensitive information from attacks and breaches.