Federated Learning (FL) is a concept first introduced by Google in 2016, in which multiple
devices collaboratively learn a machine learning model without sharing their private data …

Federated learning enables machine learning models to learn from private decentralized
data without compromising privacy. The standard formulation of federated learning produces …

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 …

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 emerged as a promising privacy-aware paradigm that allows
multiple clients to jointly train a model without sharing their private data. Recently, many …

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) allows a server to learn a machine learning (ML) model across
multiple decentralized clients that privately store their own training data. In contrast with …

Recently, federated learning (FL) has emerged as a promising distributed machine learning
(ML) technology, owing to the advancing computational and sensing capacities of end-user …

Federated learning (FL) has attracted growing interest for enabling privacy-preserving
machine learning on data stored at multiple users while avoiding moving the data off-device …