In the traditional distributed machine learning scenario, the user's private data is transmitted
between clients and a central server, which results in significant potential privacy risks. In …

Federated learning (FL) has emerged as a highly effective paradigm for privacy-preserving
collaborative training among different parties. Unlike traditional centralized learning, which …

Federated Learning (FL) is an emerging technique for collaboratively training machine
learning models on distributed data under privacy constraints. However, recent studies have …

Federated learning is a set-up in which multiple clients collaborate to solve machine
learning problems, which is under the coordination of a central aggregator. This setting also …

Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …

When data privacy is imposed as a necessity, Federated learning (FL) emerges as a
relevant artificial intelligence field for developing machine learning (ML) models in a …

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 …

The data generated and stored in mobile devices owned by individuals as well as in various
organizations contains a large amount of valuable and important information that can be …

Federated learning (FL) is a privacy-preserving distributed learning approach that allows
multiple parties to jointly build machine learning models without disclosing sensitive data …

In recent years, federated learning has attracted more and more attention as it could
collaboratively train a global model without gathering the users' raw data. It has brought …