Federated Learning (FL) has emerged in edge computing to address privacy concerns in
mobile networks. It allows the mobile devices to collaboratively train a model while keeping …

Federated learning (FL) is a kind of distributed machine learning framework, where the
global model is generated on the centralized aggregation server based on the parameters of …

Federated Learning (FL) allows training machine learning models in privacy-constrained
scenarios by enabling the cooperation of edge devices without requiring local data sharing …

Federated Learning (FL) enabled creating models that are competitive to centralized
Machine Learning models while preserving privacy by allowing clients to train data locally …

Federated Learning (FL) allows training machine learning models in privacy-constrained
scenarios by enabling the cooperation of edge devices without requiring local data sharing …

Federated learning (FL) has emerged as an effective solution to decentralized and privacy-
preserving machine learning for mobile clients. While traditional FL has demonstrated its …

Federated learning (FL) is a privacy-preserving machine learning setting that enables many
devices to jointly train a shared global model without the need to reveal their data to a …

Federated learning (FL) is a new paradigm for privacy-preserving learning. This is
particularly appealing in the mobile edge network (MEN), in which devices collectively train …

Federated Learning (FL) is a paradigm that permits to learn a Deep Learning model without
centralizing raw data, and has recently received growing interest primarily as a solution to …

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 …