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

participants, and the process is repeated until the model has 
converged. 
The key advantage of federated learning is that it enables 
the training of models on data that is distributed across 
multiple parties, such as in a healthcare setting where patient 
data may be spread across multiple hospitals or clinics. 
Federated learning also offers privacy and security benefits, as 
the raw data never leaves the devices of the participants and is 
not stored on the central server. In a clinical event 
classification task, federated learning can be used to train a 
shared model on data from multiple hospitals or clinics 
without compromising the privacy of the patients. 
Flower is a federated learning method that aims to improve 
the performance and fairness of federated learning models. It 
stands for Fairness, Accuracy, and Privacy in Federated 
Learning and is based on the concept of differential privacy. 
In the Flower federated learning method, the participants first 
locally train their models on their own data and then send their 
model parameters to the central server. The central server then 
computes a global model by aggregating the model 
parameters, while adding noise to the aggregated gradients to 
ensure differential privacy. 
The main idea behind the Flower method is to ensure that the 
model parameters are updated fairly across all participants, 
regardless of the size and quality of their data. This is achieved 
by weighing the contributions of the participants to the global 
model based on their data quality and the model performance 
on their local data. The flower has several advantages 
compared to traditional federated learning methods as the 
flower ensures fairness in the model training by weighting the 
contributions of the participants based on their data quality and 
the performance of the local model on their data. This helps to 
prevent the dominance of participants with larger and more 
diverse data, which can result in a suboptimal global model. 
Also, Flower incorporates differential privacy by adding noise 
to the aggregated gradients before sending them to the central 
server. This helps to ensure the privacy of participants’ data, 
even if the central server is com-promised. One special 
advantage is improved accuracy by weighing the contributions 
of the participants based on their data quality and the 
performance of the local model, Flower can improve the 
accuracy of the global model. This is because the model 
parameters that are contributing the most to the global model 
are updated more frequently, resulting in a more accurate 
model. Overall, the Flower federated learning method 
provides a privacy-preserving and fair solution for training 
shared models on distributed data. It is particularly useful in 
clinical settings where data is collected and stored in different 
hospitals or clinics.