15 citations · 33 across the 12 of their papers we have counts for
4 papers · 1 filter
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…