Distributed learning algorithms aim to leverage distributed and diverse data stored at users'
devices to learn a global phenomena by performing training amongst participating devices …

In federated distributed learning, the goal is to optimize a global training objective defined
over distributed devices, where the data shard at each device is sampled from a possibly …

Federated learning (FL) is a distributed learning paradigm that enables a large number of
mobile devices to collaboratively learn a model under the coordination of a central server …

Federated Learning is an emerging direction in distributed machine learning that en-ables
jointly training a model without sharing the data. Since the data is distributed across many …

Communication is a key bottleneck in federated learning where a large number of edge
devices collaboratively learn a model under the orchestration of a central server without …

Federated learning (FL) is a powerful distributed machine learning framework where a
server aggregates models trained by different clients without accessing their private data …

Federated learning aims to jointly learn statistical models over massively distributed remote
devices. In this work, we propose FedDANE, an optimization method that we adapt from …

Due to the dynamics of wireless channels and limited wireless resources (ie, spectrum),
deploying federated learning (FL) over wireless networks is challenged by frequent FL …

We propose a novel federated learning method for distributively training neural network
models, where the server orchestrates cooperation between a subset of randomly chosen …

Decentralized federated learning, inherited from decentralized learning, enables the edge
devices to collaborate on model training in a peer-to-peer manner without the assistance of …