First Analysis of Local GD on Heterogeneous Data. - Ahmed Khaled 001; Konstantin Mishchenko; Peter Richtárik (CoRR, 2019)

Gradient Descent with Compressed Iterates. - Ahmed Khaled 001; Peter Richtárik (CoRR, 2019)

Better Communication Complexity for Local SGD. - Ahmed Khaled 001; Konstantin Mishchenko; Peter Richtárik (CoRR, 2019)

Distributed Fixed Point Methods with Compressed Iterates. - Sélim Chraibi; Ahmed Khaled 001; Dmitry Kovalev; Peter Richtárik; Adil Salim; Martin Takác 001 (CoRR, 2019)

Tighter Theory for Local SGD on Identical and Heterogeneous Data. - Ahmed Khaled 001; Konstantin Mishchenko; Peter Richtárik (AISTATS, 2020)

Random Reshuffling: Simple Analysis with Vast Improvements. - Konstantin Mishchenko; Ahmed Khaled 001; Peter Richtárik (NeurIPS, 2020)

Applying fast matrix multiplication to neural networks. - Ahmed Khaled 001; Amir F. Atiya; Ahmed H. Abdel-Gawad (SAC, 2020)

FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning. - Elnur Gasanov; Ahmed Khaled 001; Samuel Horváth; Peter Richtárik (AISTATS, 2022)

Proximal and Federated Random Reshuffling. - Konstantin Mishchenko; Ahmed Khaled 001; Peter Richtárik (ICML, 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)

Faster federated optimization under second-order similarity. - Ahmed Khaled 001; Chi Jin 001 (ICLR, 2023)

DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method. - Ahmed Khaled 001; Konstantin Mishchenko; Chi Jin 001 (NeurIPS, 2023)

Better Theory for SGD in the Nonconvex World. - Ahmed Khaled 001; Peter Richtárik (Trans. Mach. Learn. Res., 2023)

Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization. - Ahmed Khaled 001; Othmane Sebbouh; Nicolas Loizou; Robert M. Gower; Peter Richtárik (J. Optim. Theory Appl., 2023)

Tuning-Free Stochastic Optimization. - Ahmed Khaled 001; Chi Jin 001 (ICML, 2024)

The Road Less Scheduled. - Aaron Defazio; Xingyu Yang; Ahmed Khaled 001; Konstantin Mishchenko; Harsh Mehta; Ashok Cutkosky (NeurIPS, 2024)

Directional Smoothness and Gradient Methods: Convergence and Adaptivity. - Aaron Mishkin; Ahmed Khaled 001; Yuanhao Wang 001; Aaron Defazio; Robert M. Gower (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)

A Novel Unified Parametric Assumption for Nonconvex Optimization. - Artem Riabinin; Ahmed Khaled 001; Peter Richtárik (CoRR, 2025)