Edge machine learning involves the deployment of learning algorithms at the wireless
network edge so as to leverage massive mobile data for enabling intelligent applications …

Next-generation wireless networks are expected to support extremely high data rates and
radically new applications, which require a new wireless radio technology paradigm. The …

This paper considers a federated edge learning (FEL) system where a base station (BS)
coordinates a set of edge devices to train a shared machine learning model collaboratively …

Under the federated learning paradigm, a set of nodes can cooperatively train a machine
learning model with the help of a centralized server. Such a server is also tasked with …

The conventional model aggregation-based federated learning (FL) approach requires all
local models to have the same architecture, which fails to support practical scenarios with …

Federated Learning (FL) is a collaborative machine learning (ML) framework that combines
on-device training and server-based aggregation to train a common ML model among …

We consider the problem of training a given machine learning model by decentralized
parallel stochastic gradient descent over training data distributed across multiple nodes …

Consider a distributed multi-task learning (MTL) framework where the distributed users first
train their own models based on the local data and then send the local updates to the server …

Federated learning (FL) has emerged as a key technique for distributed machine learning
(ML). Most literature on FL has focused on systems with (i) ML model training for a single …

Federated Learning is a machine learning setting where the goal is to train a high-quality
centralized model while training data remains distributed over a large number of clients …