Federated learning (FL) is a scheme in which several consumers work collectively to unravel
machine learning (ML) problems, with a dominant collector synchronizing the procedure …

Federal learning (FL) can realize a distributed training machine learning models in multiple
devices while protecting their data privacy, but some defect still exists such as single point …

Federated learning (FL) has experienced a boom in recent years, which is jointly promoted
by the prosperity of machine learning and Artificial Intelligence along with emerging privacy …

The federated learning technique (FL) supports the collaborative training of machine
learning and deep learning models for edge network optimization. Although a complex edge …

Federated Learning (FL) has been foundational in improving the performance of a wide
range of applications since it was first introduced by Google. Some of the most prominent …

Federated learning (FL) is a promising framework for distributed machine learning that trains
models without sharing local data while protecting privacy. FL exploits the concept of …

Blockchain technology is an undeniable ledger technology that stores transactions in high-
security chains of blocks. Blockchain can solve security and privacy issues in a variety of …

Federated Learning (FL) is a promising solution for training using data collected from
heterogeneous sources (eg, mobile devices) while avoiding the transmission of large …

With the technological advances in machine learning, effective ways are available to
process the huge amount of data generated in real life. However, issues of privacy and …

The advent of federated learning (FL) has sparked a new paradigm of parallel and
confidential decentralized machine learning (ML) with the potential of utilizing the …