Federated learning (FL) is a machine learning approach that decentralizes data and its
processing by allowing clients to train intermediate models on their devices with locally …

Federated learning has emerged as a promising approach for collaborative model training
across distributed devices. Federated learning faces challenges such as Non-Independent …

Recent years have witnessed a huge demand for artificial intelligence and machine learning
applications in wireless edge networks to assist individuals with real-time services …

Statistical heterogeneity across clients in a Federated Learning (FL) system increases the
algorithm convergence time and reduces the generalization performance, resulting in a …

Data heterogeneity is a challenge of federated learning. Traditional federated learning aims
to obtain a global model, but a single global model cannot meet the needs of all clients …

Collaborative training can improve the accuracy of a model for a user by trading off the
model's bias (introduced by using data from other users who are potentially different) against …

In the realm of future 6G wireless networks, integrating the intelligent edge through the
advent of AI signifies a momentous leap forward, promising revolutionary advancements in …

Conventional federated learning (FL) frameworks follow a server-centric model where the
server determines session initiation and client participation. We introduce Client-Centric …

O Aprendizado Federado vem ganhando força nos últimos anos, principalmente por ser
altamente escalável, podendo abrigar diferentes dispositivos, com dados heterogêneos …