In this paper, we present a new approach for ECG signal classification based on Federated
Unlearning (FUL) concept. In fact, the analysis and the processing of ECG signals are a key …

Federated learning (FL) has emerged as a promising approach for training machine
learning models on distributed data while preserving privacy specifically in the field of …

Access to a large dataset is necessary to improve disease detection with excellent accuracy.
However, due to data confidentiality and privacy restrictions, collecting data from hospitals or …

Background Federated learning (FL) is a newly proposed machine-learning method that
uses a decentralized dataset. Since data transfer is not necessary for the learning process in …

Federated Learning has garnered considerable attention in recent years due to its capability
to maintain data at its original location, thus preserving privacy and security while still …

Federated learning (FL), also referred to as collaborative learning, uses a number of
dispersed edge devices or servers to run the training algorithms, without exchanging local …

Federated learning is a framework in machine learning that allows for training a model while
maintaining data privacy. Moreover, it allows clients with their own data to collaborate in …

Federated learning is a very convenient approach for scenarios where (i) the exchange of
data implies privacy concerns and/or (ii) a quick reaction is needed. In smart healthcare …

This study distrubuted learning and machine learning to diagnose lung illnesses, is
described in this research report. The experimental design examines four different …

This paper presents the use of federated learning (FL) in healthcare to improve the efficiency
and accuracy of medical diagnosis while addressing privacy concerns related to medical …