15 citations · 33 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
Error Feedback Reloaded: From Quadratic to Arithmetic Mean of Smoothness Constants
Peter Richtárik, Elnur Gasanov, Konstantin Burlachenko
Error Feedback (EF) is a highly popular and immensely effective mechanism for fixing convergence issues which arise in distributed training methods (such as distributed GD or SGD)…
Error Feedback Shines when Features are Rare
Peter Richtárik, Elnur Gasanov, Konstantin Burlachenko
We provide the first proof that gradient descent with greedy sparsification and error feedback $\left({\…
Federated Learning with Regularized Client Participation
Grigory Malinovsky, Samuel Horváth, Konstantin Burlachenko +1
Federated Learning (FL) is a distributed machine learning approach where multiple clients work together to solve a machine learning task. One of the key challenges in FL is the iss…
Personalized Federated Learning with Communication Compression
El Houcine Bergou, Konstantin Burlachenko, Aritra Dutta +1
In contrast to training traditional machine learning (ML) models in data centers, federated learning (FL) trains ML models over local datasets contained on resource-constrained het…
Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling
Alexander Tyurin, Lukang Sun, Konstantin Burlachenko +1
We revisit the classical problem of finding an approximately stationary point of the average of smooth and possibly nonconvex functions. The optimal complexity of stochastic fi…
Federated Optimization Algorithms with Random Reshuffling and Gradient Compression
Abdurakhmon Sadiev, Grigory Malinovsky, Eduard Gorbunov +4
Gradient compression is a popular technique for improving communication complexity of stochastic first-order methods in distributed training of machine learning models. However, th…
FL_PyTorch: optimization research simulator for federated learning
Konstantin Burlachenko, Samuel Horváth, Peter Richtárik
Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared machine learning model while keeping training data locally on the de…
Faster Rates for Compressed Federated Learning with Client-Variance Reduction
Haoyu Zhao, Konstantin Burlachenko, Zhize Li +1
Due to the communication bottleneck in distributed and federated learning applications, algorithms using communication compression have attracted significant attention and are wide…
MARINA: Faster Non-Convex Distributed Learning with Compression
Eduard Gorbunov, Konstantin Burlachenko, Zhize Li +1
We develop and analyze MARINA: a new communication efficient method for non-convex distributed learning over heterogeneous datasets. MARINA employs a novel communication compressio…