We analyze asynchronous-type algorithms for distributed SGD in the heterogeneous setting,
where each worker has its own computation and communication speeds, as well as data …

Decentralized learning (DL) enables collaborative learning without a server and without
training data leaving the users' devices. However, the models shared in DL can still be used …

Access to clean water is a fundamental human need, yet millions of people worldwide still
lack access to safe drinking water. Traditional water quality assessments, though reliable …

Decentralized Federated Learning (DFL) has received significant recent research attention,
capturing settings where both model updates and model aggregations--the two key FL …

Decentralized learning (DL) is an emerging technique that allows nodes on the web to
collaboratively train machine learning models without sharing raw data. Dealing with …

We consider the distributed learning problem with data dispersed across multiple workers
under the orchestration of a central server. Asynchronous Stochastic Gradient Descent …

Asynchronous stochastic gradient descent (ASGD) is a computationally efficient algorithm,
which speeds up deep learning training and plays an important role in distributed deep …