3 papers
cs.LG2025
Optimization Methods and Software for Federated Learning
Konstantin Burlachenko
Federated Learning (FL) is a novel, multidisciplinary Machine Learning paradigm where multiple clients, such as mobile devices, collaborate to solve machine learning problems. Init…
cs.LG2025
BurTorch: Revisiting Training from First Principles by Coupling Autodiff, Math Optimization, and Systems
Konstantin Burlachenko, Peter Richtárik
In this work, we introduce BurTorch, a compact high-performance framework designed to optimize Deep Learning (DL) training on single-node workstations through an exceptionally effi…
cs.LG2024
Unlocking FedNL: Self-Contained Compute-Optimized Implementation
Konstantin Burlachenko, Peter Richtárik
Federated Learning (FL) is an emerging paradigm that enables intelligent agents to collaboratively train Machine Learning (ML) models in a distributed manner, eliminating the need…