Type of the Paper Article

Keywords: Federated learning, clinical events, vital signs, classification, multimodal.
1. Introduction
Artificial intelligence (AI) techniques and technologies are used to improve various aspects of healthcare. This can include medical imaging, drug discovery, patient diagnosis, and treatment planning [1]. There is a growing body of research in this field, as AI can significantly improve the efficiency and accuracy of healthcare processes, ultimately leading to better patient outcomes. Some examples of related work include using AI to diagnose 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 recently become a buzzword in many industries, 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]. Such 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 initial 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 predict 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 a considerable improvement in the healthcare system, as mentioned above, privacy has been the main issue concerning big data, especially in the healthcare system. In addition, enhanced machine learning techniques and advanced pre-processing can be a positive approach to solving a problem using big data.
Machine learning, a branch of artificial intelligence, entails training computer algorithms to identify patterns within data and utilize those patterns to make informed decisions. In healthcare, machine learning is used to analyze substantial 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 can be used to analyze patient data to predict future health outcomes, such as the likelihood of developing a specific condition or needing medical intervention. This can help healthcare providers make more informed decisions about patient care and allocate resources more efficiently in understanding the geographical inequalities of healthcare resources with Bayesian analysis [6], clinical data prediction using Random Forest classification [7], and disease pre-diction with XGBoost classification [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 analyze medical images, such as CT scans or X-rays, to assist in diagnosis and treatment planning. It can also 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 their genetics, lifestyle, and medical history [11].
F ederated learning (FL) [12] trains machine learning models on decentralized data. Instead of centralizing data in an individual 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. A global model is repeatable until it reaches a satisfactory level of performance. This allows for training on much larger datasets than possible with a centralized approach and helps protect users' privacy by keeping their data on their own cross devices such as electronic health records (EHRs), wearable devices (e.g., smartwatches and fitness trackers), and medical imaging devices. In the case of Federated Learning, cross-device functionality allows each of these devices to contribute to the learning process by training their own local models on the data they have, and then sharing the model parameters with a central server. The server then aggregates these parameters to update the global model, which is then sent back to each device. Figure 1 shows the general architecture of using Federated learning in the healthcare system with components and connection with FL.