Date of publication XXXX 00, 0000, date of current version XXXX 00, 0000

Figure 1. The general concept of Federated learning in the healthcare system
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can be used to analyze patient data to make predictions about 
future health outcomes, such as the likelihood of developing a 
certain condition or the likelihood of needing medical 
intervention. This can help healthcare providers make more 
informed decisions about patient care and allocate resources 
more efficiently. For example, understanding geographical 
inequalities of healthcare resources with Bayesian analysis [6], 
clinical data prediction using Random Forest classification [7], 
disease pre-diction with XGBoots regression [8]. Clinical 
decision support: Machine learning can be used to develop 
clinical decision support systems, which provide healthcare 
providers with real-time recommendations based on a patient's 
medical history and current condition [9]. Diagnosis and 
treatment: Machine learning can be used to analyze medical 
images, such as CT scans or X-rays, to assist in diagnosis and 
treatment planning. It can also be used to analyze lab test 
results to identify potential health issues [10]. Personalized 
medicine: Machine learning can be used to develop 
personalized treatment plans for individual patients, 
considering factors such as their genetics, lifestyle, and 
medical history [11]. 
Federated learning (FL) [12] is a method of training 
machine learning models on decentralized data. Instead of 
centralizing data in a single location, federated learning allows 
data to remain on individual devices, such as smartphones or 
IoT devices. The model is trained across multiple devices by 
sending model updates to each device and receiving updated 
parameters back from each device. A global model is 
repeatable until it reaches a satisfactory level of performance. 
This allows for training on much larger datasets than would be 
possible with a centralized approach and helps to protect users' 
privacy by keeping their data on their own devices. Figure 1 
shows the general concept of Federated learning in the 
healthcare system. 
Although, federated learning has the potential to be