Federated learning (FL) is an efficient and privacy-preserving distributed learning paradigm
that enables massive edge devices to train machine learning models collaboratively …

In federated learning (FL), local workers learn a global model collaboratively using their
local data by communicating trained models to a central server for privacy concerns. Due to …

There is an increasing interest in a fast-growing machine learning technique called
Federated Learning (FL), in which the model training is distributed over mobile user …

Federated learning (FL) can generate huge communication overhead for the central server,
which may cause operational challenges. Furthermore, the central server's failure or …

Federated learning (FL) is a distributed machine learning technology for next-generation AI
systems that allows a number of workers, ie, edge devices, collaboratively learn a shared …

Most existing wireless federated learning (FL) studies focused on homogeneous model
settings where devices train identical local models. In this setting, the devices with poor …

Federated learning (FL) is a framework of large-scale distributed learning with user privacy
protection through local training and global aggregation. However, FL may suffer from …

Federated learning (FL) has prevailed as an efficient and privacy-preserved scheme for
distributed learning. In this work, we mainly focus on the optimization of computation and …

Federated learning (FL) is a promising distributed learning paradigm, which can effectively
avoid the privacy leakage and communication issues compared with the centralized …

Federated learning (FL) enables edge devices, such as Internet of Things devices (eg,
sensors), servers, and institutions (eg, hospitals), to collaboratively train a machine learning …