In data-parallel optimization of machine learning models, workers collaborate to improve
their estimates of the model: more accurate gradients allow them to use larger learning rates …

One of the key challenges in decentralized and federated learning is to design algorithms
that efficiently deal with highly heterogeneous data distributions across agents. In this paper …

Recently, mobile and embedded devices worldwide generate a massive amount of data at
the network edge. To efficiently exploit the data from distributed devices, we concentrate on …

Protecting data privacy is a significant challenge in machine learning (ML), and federated
learning (FL) has emerged as a decentralized learning solution to address this issue …

The convergence speed of machine learning models trained with Federated Learning is
significantly affected by heterogeneous data partitions, even more so in a fully decentralized …

We consider a collaborative learning setting where the goal of each agent is to improve their
own model by leveraging the expertise of collaborators, in addition to their own training data …

Recently, a shift has been observed towards the so-called edge machine learning, which
allow multiple devices with local computational and storage resources to collaborate with …

Decentralized learning enables serverless training of deep neural networks (DNNs) in a
distributed manner on multiple nodes. This allows for the use of large datasets, as well as …

In data-parallel optimization of machine learning models, workers collaborate to improve
their estimates of the model: more accurate gradients allow them to use larger learning rates …

Recently, local peer topology has been shown to influence the overall convergence of
decentralized learning (DL) graphs in the presence of data heterogeneity. In this paper, we …