Federated learning is a method of training a global model from decentralized data
distributed across client devices. Here, model parameters are computed locally by each …

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

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 …

Federated Learning (FL) is a promising decentralized machine learning technique, which
can be efficiently used to reduce the latency and deal with the data privacy in the next 6th …

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

We study federated learning (FL), where power-limited wireless devices utilize their local
datasets to collaboratively train a global model with the help of a remote parameter server …

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

Major bottlenecks of large-scale Federated Learning (FL) networks are the high costs for
communication and computation. This is due to the fact that most of current FL frameworks …

Federated learning (FL) is a decentralized algorithm that can train a globally shared model
without the requirement to send the raw data to a centralized server by user equipments …