Recently, federated learning has gained increasing attention for privacy-preserving
computation since the learning paradigm allows to train models without the need for …

Federated learning (FL) is a privacy-preserving learning framework, which collaboratively
learns a centralized model across edge devices. Each device trains an independent model …

Federated learning (FL) is a distributed deep learning method which enables multiple
participants, such as mobile phones and IoT devices, to contribute a neural network model …

Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving
distributed Machine Learning (ML), enabling model training across distributed devices …

In the era of advanced technologies, mobile devices are equipped with computing and
sensing capabilities that gather excessive amounts of data. These amounts of data are …

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

The longstanding goals of federated learning (FL) require rigorous privacy guarantees and
low communication overhead while holding a relatively high model accuracy. However …

Federated learning (FL) has emerged as a promising privacy-preserving distributed
machine learning framework recently. It aims at collaboratively learning a shared global …

Deep learning has been applied in many areas, such as computer vision, natural language
processing and emotion analysis. Differing from the traditional deep learning that collects …

Federated learning (FL) is a promising paradigm to enable privacy-preserving deep learning
from distributed data. Most previous works are based on federated average (FedAvg), which …