Federated learning (FL) has become the next generation of machine learning (ML) by
avoiding local data sharing with a central server. While this becomes a major advantage to …

The potential of Federated Learning (FL) deployment increases rapidly as the number of
connected devices increases, the value of artificial intelligence is recognized 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 …

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 …

With the expansion of the Internet of Things (IoT) development and application, federated
learning has gained higher popularity in industrial researching fields. However, the security …

To be able to train a model in a way where access to datasets remains private, Federated
Learning (FL) was proposed as a machine learning technique to address the privacy …

Federated learning (FL) is a technique that involves multiple participants who update their
local models with private data and aggregate these models using a central server …

Privacy and data security have become the new hot topic for regulators in recent years. As a
result, Federated Learning (FL)(also called collaborative learning) has emerged as a new …

Federated learning (FL) has nourished a promising method for data silos, which enables
multiple participants to construct a joint model collaboratively without centralizing data. The …

In federated learning (FL), multiple clients use local datasets to train models and submit
local gradients to the server for aggregation. However, malicious clients may compromise …