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

INDEX TERMS 
Federated learning, clinical events, vital signs, classification.
I.
INTRODUCTION 
The use of artificial intelligence (AI) techniques and 
technologies to improve various aspects of healthcare. This 
can include areas such as medical imaging, drug discovery, 
patient diagnosis, and treatment planning [1]. There is a 
growing body of research in this field, as AI has the potential 
to significantly improve the efficiency and accuracy of 
healthcare processes, and ultimately lead to better patient 
outcomes. Some examples of related work include using AI to 
assist in the diagnosis of diseases such as cancer, using 
machine learning to analyze patient data and predict potential 
health issues, and using natural language processing to 
improve the efficiency of electronic medical records. 
Big data [1] has become a buzzword in many industries in 
recent years, and healthcare is no exception. The healthcare 
sector generates vast amounts of data daily, including 
electronic health records, claims data, and clinical trial results 
[2,3]. This data can be analyzed to identify patterns, trends, 
and associations that can help improve patient care, reduce 
costs, and advance medical research. The use of big data in 
healthcare is still in its early stages, but it has already shown 
promise in several areas. For example, big data has been used 
to improve population health management by identifying 
patterns in patient health data that can help healthcare 
providers better understand the health needs of their patient 
population and develop strategies to improve population 
health. Big data has also been used to make predictions about 
future patient needs and outcomes using predictive analytics, 
and to develop clinical decision support systems that provide 
healthcare providers with real-time recommendations based 
on a patient's medical history and current condition. [4] 
Although there is huge improvement of the healthcare system 
as we mentioned above, privacy has been the main issue 
among big data and especially in the healthcare system. In 
addition, while using big data, enhanced machine learning 
techniques and advanced pre-processing can be a positive 
approach to solving the problem. 
Machine learning is a type of artificial intelligence that 
involves training computer algorithms to recognize patterns in 
data and make decisions based on those patterns. In healthcare, 
machine learning is being used to analyze large amounts of 
data from various sources, such as electronic health records, 
medical imaging, and wearable devices, to identify patterns 
and trends that can help improve patient care [5]. Predictive 
analytics: Machine learning algorithms

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Ruzaliev R: 
Federated Learning for Clinical Event Classification Using Vital 
Signs Data 
2 
VOLUME XX, 2023