We study normalized error feedback algorithms with momentum and parameter-agnostic stepsizes, eliminating the need for problem-dependent tuning while achieving competitive convergence rates.
Nov 1, 2025
We introduce Bernoulli-LoRA, a theoretical framework for randomized Low-Rank Adaptation that unifies existing approaches and provides convergence guarantees for various optimization methods.
Aug 1, 2025
We extend MARINA-P algorithm to non-smooth federated optimization, providing the first theoretical analysis with server-to-worker compression and adaptive stepsizes while achieving optimal convergence rates.
Dec 22, 2024
We propose a novel federated learning approach that allows multiple communication rounds per cohort, achieving up to 74% reduction in total communication costs through a new stochastic proximal point method variant.
Jun 1, 2024
This work introduces novel methods combining random reshuffling with gradient compression for distributed and federated learning, providing theoretical analysis and practical improvements over existing approaches.
Jun 14, 2022
We introduce three point compressors (3PC), a novel framework unifying and improving communication-efficient distributed optimization methods. Presented as a Poster at ICML 2022.
Feb 2, 2022
Six practical algorithmic extensions of the EF21 error feedback method for communication-efficient distributed learning, with strong convergence guarantees.
Oct 7, 2021
We propose EF21, a novel approach to error feedback offering a better theoretical rate and strong empirical results. Presented as an Oral + Poster at NeurIPS 2021.
Jun 9, 2021