Federated learning (FL) is a distributed machine learning approach where multiple clients
collaboratively train a joint model without exchanging their own data. Despite FL's …

Abstract Federated Learning (FL) enhances Artificial Intelligence (AI) applications by
enabling individual devices to collaboratively learn shared models without uploading local …

As an emerging technique, Federated Learning (FL) can jointly train a global model with the
data remaining locally, which effectively solves the problem of data privacy protection …

Federated learning (FL) has been widely acknowledged as a promising solution to training
machine learning (ML) model training with privacy preservation. To reduce the traffic …

The aim of Machine Unlearning (MU) is to provide theoretical guarantees on the removal of
the contribution of a given data point from a training procedure. Federated Unlearning (FU) …

The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …

Federated Learning (FL) is a distributed learning paradigm that enables mutually untrusting
clients to collaboratively train a common machine learning model. Client data privacy is …

Federated Learning (FL) is a paradigm that permits to learn a Deep Learning model without
centralizing raw data, and has recently received growing interest primarily as a solution to …

As a new distributed machine learning (ML) framework for privacy protection, federated
learning (FL) enables substantial Internet of Things (IoT) devices (eg, mobile phones …

Federated Learning has emerged as a dominant computational paradigm for distributed
machine learning. Its unique data privacy properties allow us to collaboratively train models …