From Local SGD to Local Fixed-Point Methods for Federated Learning. - Grigory Malinovskiy; Dmitry Kovalev; Elnur Gasanov; Laurent Condat; Peter Richtárik (ICML, 2020)

Distributed Proximal Splitting Algorithms with Rates and Acceleration. - Laurent Condat; Grigory Malinovsky; Peter Richtárik (CoRR, 2020)

ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! - Konstantin Mishchenko; Grigory Malinovsky; Sebastian U. Stich; Peter Richtárik (ICML, 2022)

Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning. - Grigory Malinovsky; Kai Yi; Peter Richtárik (NeurIPS, 2022)

Federated Random Reshuffling with Compression and Variance Reduction. - Grigory Malinovsky; Peter Richtárik (CoRR, 2022)

Federated Optimization Algorithms with Random Reshuffling and Gradient Compression. - Abdurakhmon Sadiev; Grigory Malinovsky; Eduard Gorbunov; Igor Sokolov 001; Ahmed Khaled 001; Konstantin Burlachenko; Peter Richtárik (CoRR, 2022)

Can 5th Generation Local Training Methods Support Client Sampling? Yes! - Michal Grudzien; Grigory Malinovsky; Peter Richtárik (CoRR, 2022)

Can 5th Generation Local Training Methods Support Client Sampling? Yes! - Michal Grudzien; Grigory Malinovsky; Peter Richtárik (AISTATS, 2023)

Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization. - Grigory Malinovsky; Konstantin Mishchenko; Peter Richtárik (DistributedML@CoNEXT, 2023)

A Guide Through the Zoo of Biased SGD. - Yury Demidovich; Grigory Malinovsky; Igor Sokolov 001; Peter Richtárik (NeurIPS, 2023)

Random Reshuffling with Variance Reduction: New Analysis and Better Rates. - Grigory Malinovsky; Alibek Sailanbayev; Peter Richtárik (UAI, 2023)

Federated Learning with Regularized Client Participation. - Grigory Malinovsky; Samuel Horváth; Konstantin Burlachenko; Peter Richtárik (CoRR, 2023)

TAMUNA: Accelerated Federated Learning with Local Training and Partial Participation. - Laurent Condat; Grigory Malinovsky; Peter Richtárik (CoRR, 2023)

Improving Accelerated Federated Learning with Compression and Importance Sampling. - Michal Grudzien; Grigory Malinovsky; Peter Richtárik (CoRR, 2023)

Byzantine Robustness and Partial Participation Can Be Achieved Simultaneously: Just Clip Gradient Differences. - Grigory Malinovsky; Peter Richtárik; Samuel Horváth; Eduard Gorbunov (CoRR, 2023)

Minibatch Stochastic Three Points Method for Unconstrained Smooth Minimization. - Soumia Boucherouite; Grigory Malinovsky; Peter Richtárik; El Houcine Bergou (AAAI, 2024)

Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences. - Grigory Malinovsky; Peter Richtárik; Samuel Horváth; Eduard Gorbunov (NeurIPS, 2024)

MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence. - Ionut-Vlad Modoranu; Mher Safaryan; Grigory Malinovsky; Eldar Kurtic; Thomas Robert 007; Peter Richtárik; Dan Alistarh (NeurIPS, 2024)

Don't Compress Gradients in Random Reshuffling: Compress Gradient Differences. - Abdurakhmon Sadiev; Grigory Malinovsky; Eduard Gorbunov; Igor Sokolov 001; Ahmed Khaled 001; Konstantin Burlachenko; Peter Richtárik (NeurIPS, 2024)

Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction. - Yury Demidovich; Grigory Malinovsky; Peter Richtárik (CoRR, 2024)

Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation. - Grigory Malinovsky; Umberto Michieli; Hasan Abed Al Kader Hammoud; Taha Ceritli; Hayder Elesedy; Mete Ozay; Peter Richtárik (CoRR, 2024)

MAST: model-agnostic sparsified training. - Yury Demidovich; Grigory Malinovsky; Egor Shulgin; Peter Richtárik (ICLR, 2025)

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization. - Yury Demidovich; Petr Ostroukhov; Grigory Malinovsky; Samuel Horváth; Martin Takác 001; Peter Richtárik; Eduard Gorbunov (ICLR, 2025)

An Optimal Algorithm for Strongly Convex Min-Min Optimization. - Dmitry Kovalev; Alexander V. Gasnikov; Grigory Malinovsky (UAI, 2025)