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

Many of the machine learning tasks are focused on centralized learning (CL), which requires
the transmission of local datasets from the clients to a parameter server (PS) leading to a …

Future wireless networks are expected to support diverse mobile services, including artificial
intelligence (AI) services and ubiquitous data transmissions. Federated learning (FL), as a …

Owing to the increasing need for massive data analysis and model training at the network
edge, as well as the rising concerns about the data privacy, a new distributed training …

Federated learning (FL) is an emerging machine learning paradigm for training models
across multiple edge devices holding local data sets, without explicitly exchanging the data …

Federated Learning (FL) is a newly emerged decentralized machine learning (ML)
framework that combines on-device local training with server-based model synchronization …

Federated edge learning (FL), as an emerging distributed machine learning paradigm,
allows a mass of edge devices to collaboratively train a global model while preserving …

Federated learning (FL) is a new paradigm for large-scale learning tasks across mobile
devices. However, practical FL deployment over resource constrained mobile devices …

Federated learning (FL) is able to manage edge devices to cooperatively train a model while
maintaining the training data local and private. One common assumption in FL is that all …

Communication efficiency is of importance for wireless federated learning systems. In this
paper, we propose a communication-efficient strategy for federated learning over multiple …