5 citations · 16 across the 5 of their papers we have counts for
3 papers · 1 filter
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…
Can 5th Generation Local Training Methods Support Client Sampling? Yes!
Michał Grudzień, Grigory Malinovsky, Peter Richtárik
The celebrated FedAvg algorithm of McMahan et al. (2017) is based on three components: client sampling (CS), data sampling (DS) and local training (LT). While the first two are rea…
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning
Grigory Malinovsky, Kai Yi, Peter Richtárik
We study distributed optimization methods based on the {\em local training (LT)} paradigm: achieving communication efficiency by performing richer local gradient-based training on…