The federated learning (FL) approach in machine learning preserves user privacy during
data collection. However, traditional FL schemes still rely on a centralized server, making …

Federated learning (FL) is a promising framework for distributed machine learning that trains
models without sharing local data while protecting privacy. FL exploits the concept of …

Federated learning (FL) is a promising framework for distributed machine learning that trains
models without sharing local data while protecting privacy. FL exploits the concept of …

Federated Learning (FL) has recently arisen as a revolutionary approach to collaborative
training Machine Learning models. According to this novel framework, multiple participants …

In the era of deep learning, federated learning (FL) presents a promising approach that
allows multi-institutional data owners, or clients, to collaboratively train machine learning …

Federated Learning (FL) has recently arisen as a revolutionary approach to collaborative
training Machine Learning models. According to this novel framework, multiple participants …

Federated Learning (FL) has made an essential step towards enhancing the privacy of
traditional model training. However, gaps in the conventional FL framework make it …

Federated Learning (FL), as an emergent paradigm in privacy-preserving machine learning,
has garnered significant interest from scholars and engineers across both academic and …

Federated learning (FL) is a promising distributed machine learning architecture that allows
participants to cooperatively train a global model without sharing local data. However, both …

This paper introduces PureFed, an innovative Federated Learning (FL) framework designed
for efficiency, collaboration, and trustworthiness. In the background of FL research, it was …