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

V.
Conclusion 
The classification of clinical events using vital signs data 
is a crucial task in healthcare, as it allows for early detection 
and management of various medical conditions. Our study 
employed Federated Learning to classify clinical events 
using vital signs data, utilizing datasets from multiple clients 
of X hospital, employing various machine learning 
regression models, such as Random Forest Regression, 
XGBoost Regression, and Logistic Regression. Our use of 
Flower Federated Learning (FL) offered several advantages 
for clinic event classification, including privacy-preserving 
capabilities, enabling collaboration between multiple parties 
to train machine learning models, while safeguarding the 
privacy of each party's data. This is due to the fact that each 
party is only required to share encrypted model updates with 
other parties, rather than sharing raw data. Furthermore, 
Flower FL is designed to scale to a large number of 
participants, making it particularly suitable for clinic event 
classification problems where multiple hospitals or clinics 
may have data to contribute. By combining data and insights 
from multiple parties, Flower FL can help to improve the 
performance of the machine learning model for clinic event 
classification. This is because the model can leverage the 
combined data and insights from various sources. By 
aggregating model updates from multiple parties, Flower FL 
can help make machine learning models for clinic event 
classification more robust and less susceptible to overfitting 
to a single party's data. 
Overall, our study demonstrated that the use of Flower FL 
for clinic event classification with machine learning 
classification can significantly improve the performance and 
robustness of the model while preserving the privacy of each