Engineering Commons^™

Distributed Learning Algorithms: Communication Efficiency And Error Resilience, Raj Kumar Maity

Doctoral Dissertations

In modern day machine learning applications such as self-driving cars, recommender systems, robotics, genetics etc., the size of the training data has grown to the point that it has become essential to design distributed learning algorithms. A general framework for the distributed learning is \emph{data parallelism} where the data is distributed among the \emph{worker machines} for parallel processing and computation to speed up learning. With billions of devices such as cellphones, computers etc., the data is inherently distributed and stored locally in the users' devices. Learning in this set up is popularly known as \emph{Federated Learning}. The speed-up due to …