In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …

Abstract In 2016, Google proposed Federated Learning (FL) as a novel paradigm to train
Machine Learning (ML) models across the participants of a federation while preserving data …

In recent years, federated learning (FL) has become a very popular paradigm for training
distributed, large-scale, and privacy-preserving machine learning (ML) systems. In contrast …

Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

Federated learning (FL) is a new technology that has been a hot research topic. It enables
the training of an algorithm across multiple decentralized edge devices or servers holding …

Driven by privacy concerns and the visions of deep learning, the last four years have
witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …

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 …

Federated Learning (FL) enables distributed training by learners using local data, thereby
enhancing privacy and reducing communication. However, it presents numerous challenges …

The ongoing deployments of the Internet of Things (IoT)-based smart applications are
spurring the adoption of machine learning as a key technology enabler. To overcome the …