software development

Error Feedback for Communication-Efficient First and Second-Order Distributed Optimization: Theory and Practical Implementation

Konstantin Burlachenko, Ph.D. Student, Computer Science

May 12, 12:00 - 13:00

B9 L2 R2325

Federated learning software development

This seminar will discuss advancements in Federated Learning, including theoretical improvements to the Error Feedback method (EF21) for communication-efficient distributed training and the development of significantly more practical and efficient implementations of the Federated Newton Learn (FedNL) algorithm.

Konstantin Burlachenko

Ph.D. Student, Computer Science

Federated learning Optimization for Machine Learning High Performance Computing software development

Konstantin's research focuses on large-scale mathematical optimization systems for machine learning and artificial intelligence, especially those requiring high-performance computing approaches.