Decentralized learning enables edge users to collaboratively train models by exchanging
information via device-to-device communication, yet prior works have been limited to …

This paper proposes a communication strategy for decentralized learning on wireless
systems. Our discussion is based on the decentralized parallel stochastic gradient descent …

In this work, we focus on the communication aspect of decentralized learning, which
involves multiple agents training a shared machine learning model using decentralized …

Decentralized stochastic gradient descent (SGD) has recently become one of the most
promising methods to use data parallelism in order to train a machine learning model on a …

Distributed machine learning has been extensively employed in wireless systems, which
can leverage abundant data distributed over massive devices to collaboratively train a high …

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 …

Decentralized learning is essential for large-scale deep learning due to its great advantage
in breaking the communication bottleneck. Most decentralized learning algorithms focus on …

Decentralized learning algorithms empower interconnected devices to share data and
computational resources to collaboratively train a machine learning model without the aid of …

Consensus-based decentralized stochastic gradient descent (D-SGD) is a widely adopted
algorithm for decentralized training of machine learning models across networked agents. A …

This work focuses on the decentralized deep learning optimization framework. We propose
Adjacent Leader Decentralized Gradient Descent (AL-DSGD), for improving final model …