## 1 Introduction  
Current research on disease risk prediction mainly uses physiological indicators and risk factors to predict disease risk [1], images to predict disease risk [2], and audio to predict disease risk [3]. These studies have achieved good results. In these studies, the risk of diabetes was predicted with 99.8% accuracy, the risk of Parkinson's disease was predicted with 98% accuracy, and the risk of laryngeal air cyst was predicted with 98.5% accuracy. Some researchers use the prediction of mean arterial blood pressure in patients with sepsis to assist in treating septic shock [4]. The current application scenarios of disease risk prediction models mainly include hospitals, health management centers, insurance institutions, and elderly care institutions. Hospitals use disease risk prediction models to assist doctors in providing medical services to patients. The health management center uses disease risk prediction models to predict health risks for users and develops health intervention plans based on health analysis reports. Insurance institutions use disease risk prediction models to assist staff in making business risk judgments. Elderly care institutions use disease risk prediction models to periodically provide disease risk predictions to the elderly, improving their health status and reducing the pressure on public health services. Although the application scenarios of disease risk prediction models are diverse, according to our research, these application scenarios also have standard rules. When designing and implementing disease risk prediction models using centralized computing methods, these application scenarios must go through model design, sample processing,model training, and optimization. In the sample processing stage, when different institutions or enterprises use centralized computing methods, they will summarize the data of each branch unit. This method increases the overhead of data storage space and brings hidden dangers to data transmission security and integrity. In the model training and optimization phase, different institutions or enterprises use central server calculations when using centralized computing methods. This method increases the burden on the central server and wastes edge server resources. The usage of our proposed distributed intelligent edge computing technology for disease risk prediction is shown in Figure 1.  
As shown in Figure 1, the lightweight neural network model Linge (Lightweight Neural Network Models for the Edge) we designed will be deployed on the enterprise or institution's central server and each branch unit's edge servers. The prediction model's training and inference are completed on each branch unit's edge servers. The prediction model on the edge server of each branch unit will upload the model weights output to the central server after training and download the latest model parameters from the central server. During the inference process of the prediction model, disease risk prediction experts evaluate and correct the inference results of the model on the central server and each edge server. Using this distributed intelligent edge computing method to train disease risk prediction models does not increase data storage space, completely eliminates hidden dangers in data transmission security and data transmission integrity, and does not increase computing pressure on the central server. The research goal of this article is to achieve effective prediction of disease risk in diverse disease risk prediction application scenarios based on federated learning algorithms, edge computing, the MRI (Magnetic Resonance Imaging) image preprocessing method designed in this article, and lightweight neural network models. In this article's research on effective disease risk prediction, the main work carried out is as follows:  
* To further improve the quality of the data set, reduce the cost of feature extraction by the model, and improve the model's accuracy, we designed an MRI image preprocessing method.