Federated learning has attracted attention in recent years for collaboratively training data on
distributed devices with privacy-preservation. The limited network capacity of mobile and IoT …

In recent years, federated learning (FL) plays an important role in data privacy-sensitive
scenarios to perform learning works collectively without data exchange. However, due to the …

Federated Learning (FL) is a promising paradigm widely used in privacy-preserving
machine learning. It enables distributed devices to collaboratively train a model while …

Recently, a new distributed learning scheme called Federated Learning (FL) has been
introduced. FL is designed so that server never collects user-owned data meaning it is great …

As increasing concerns have arisen on privacy leakage in data-driven smart services,
federated learning (FL) has been introduced to collaboratively learn an efficient model …

Federated learning (FL) has become one of the mainstream paradigms for multi-party
collaborative learning with privacy protection. As it is difficult to guarantee all FL devices to …

Federated Learning (FL) has been considered as an appealing framework to tackle data
privacy issues of mobile devices compared to conventional Machine Learning (ML). Using …

Federated learning (FL) is a promising framework for privacy-preserving collaborative
learning, where model training tasks are distributed to clients and only the model updates …

Recently academia and industry has growing interest in the sixth generation network, which
aims to support a rich range of applications with higher capacity and greater coverage than …

Federated Learning (FL) has emerged as a privacy-preserving paradigm for collaborative
deep learning model training across distributed data silos. Despite its importance, FL faces …