Federated learning (FL) enables collaborative training of a machine learning (ML) model
across multiple parties, facilitating the preservation of users' and institutions' privacy by …

Federated Learning (FL) is a collaborative training paradigm whereby a global Machine
Learning (ML) model is trained using typically private and distributed data sources without …

Federated Learning (FL) is an emerging paradigm that allows a model to be trained across a
number of participants without sharing data. Recent works have begun to consider the …

Federated Learning (FL) has recently emerged as a novel machine learning paradigm
allowing to preserve privacy and to account for the distributed nature of the learning process …

Edge-AI, the convergence of edge computing and artificial intelligence (AI), has become a
promising paradigm that enables the deployment of advanced AI models at the network …

As Federated Learning (FL) has gained increasing attention, it has become widely
acknowledged that straightforwardly applying stochastic gradient descent (SGD) on the …

In continual learning applications on-the-edge multiple self-centered devices (SCD) learn
different local tasks independently, with each SCD only optimizing its own task. Can we …

The advancement of machine learning in industrial applications has necessitated the
development of tailored solutions to address specific challenges, particularly in multi-class …

Federated Learning (FL) is a novel approach that allows for collaborative machine learning
while preserving data privacy by leveraging models trained on decentralized devices …

Current Federated Learning (FL) methods often struggle with high statistical heterogeneity
across clients' data, resulting in client drift due to biased local solutions. This issue is …