Federated learning (FL) is a promising privacy-preserving solution to build powerful AI
models. In many FL scenarios, such as healthcare or smart city monitoring, the user's …

Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …

Federated learning (FL) is a privacy-preserving learning technique that enables distributed
computing devices to train shared learning models across data silos collaboratively. Existing …

Federated learning (FL) enables participants to collaboratively construct a global machine
learning model without sharing their local training data to the remote server. In FL systems …

Federated learning (FL) provides a higher privacy guarantee for data sharing in a multi-party
computation environment. However, how to invite participants to federated training if they …

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

Abstract Federated Learning (FL) enhances Artificial Intelligence (AI) applications by
enabling individual devices to collaboratively learn shared models without uploading local …

Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …

Federated learning (FL) is a novel distributed privacy-preserving learning paradigm, which
enables the collaboration among several participants (eg, Internet of Things (IoT) devices) …

Federated learning (FL) emerges as a promising paradigm to enable a federation of clients
to train a machine learning model in a privacy-preserving manner. Most existing works …