Federated learning is promising in enabling large-scale machine learning by massive
clients without exposing their raw data. It can not only enable the clients to preserve the …

Federated Learning (FL) has emerged as a privacy-preserving distributed machine learning
paradigm. To motivate data owners to contribute towards FL, research on FL incentive …

Federated learning utilizes various resources provided by participants to collaboratively train
a global model, which potentially address the data privacy issue of machine learning. In …

In federated learning (FL), a federation distributedly trains a collective machine learning
model by leveraging privacy preserving technologies. However, FL participants need to …

Federated learning is a set-up in which multiple clients collaborate to solve machine
learning problems, which is under the coordination of a central aggregator. This setting also …

Federated learning (FL), arising as a privacy-preserving machine learning paradigm, has
received notable attention from the public. In each round of synchronous FL training, only a …

Federated learning came into being with the increasing concern of privacy security, as
people's sensitive information is being exposed under the era of big data. It is an algorithm …

In federated learning (FL), data owners" share" their local data in a privacy preserving
manner in order to build a federated model, which in turn, can be used to generate revenues …

Federated learning (FL) is an emerging distributed machine learning (ML) paradigm with
enhanced privacy, aiming to achieve a" good" ML model for as many as participants while …

As a privacy-preserving paradigm for training machine learning (ML) models, federated
learning (FL) has received tremendous attention from both industry and academia. In a …