Publications by Anastasiia Koloskova

Decentralized gradient tracking with local steps

Gradient tracking (GT) is an algorithm designed for solving decentralized optimization problems over a network (such as training a machine learning model). A key feature of GT is a tracking mechanism that allows us to overcome data heterogeneity between no ...

2024

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Anastasiia Koloskova

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

EPFL2024

The Privacy Power of Correlated Noise in Decentralized Learning

Rachid Guerraoui, Martin Jaggi, Anastasiia Koloskova, Youssef Allouah, Aymane El Firdoussi

Decentralized learning is appealing as it enables the scalable usage of large amounts of distributed data and resources (without resorting to any central entity), while promoting privacy since every user minimizes the direct exposure of their data. Yet, wi ...

PMLR2024

Consensus Control for Decentralized Deep Learning

Tao Lin, Martin Jaggi, Sebastian Urban Stich, Anastasiia Koloskova, Lingjing Kong

Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters. Experiments in earlier works reveal that, even in a data-center setup, decentralized training often suffers fro ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

A Unified Theory of Decentralized SGD with Changing Topology and Local Updates

Martin Jaggi, Sebastian Urban Stich, Anastasiia Koloskova

Decentralized stochastic optimization methods have gained a lot of attention recently, mainly because of their cheap per iteration cost, data locality, and their communication-efficiency. In this paper we introduce a unified convergence analysis that cover ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2020

A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free!

Martin Jaggi, Anastasiia Koloskova

Decentralized optimization methods enable on-device training of machine learning models without a central coordinator. In many scenarios communication between devices is energy demanding and time consuming and forms the bottleneck of the entire system. We ...

MICROTOME PUBLISHING2020

Efficient Greedy Coordinate Descent for Composite Problems

Sai Praneeth Reddy Karimireddy, Martin Jaggi, Sebastian Urban Stich, Anastasiia Koloskova

Coordinate descent with random coordinate selection is the current state of the art for many large scale optimization problems. However, greedy selection of the steepest coordinate on smooth problems can yield convergence rates independent of the dimension ...

2019

Decentralized deep learning with arbitrary communication compression

Tao Lin, Martin Jaggi, Sebastian Urban Stich, Anastasiia Koloskova

Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks, as well as for efficient scaling to large compute clusters. As current approaches are limited by network bandwidth, we propose t ...

2019

Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication

Martin Jaggi, Sebastian Urban Stich, Anastasiia Koloskova

We consider decentralized stochastic optimization with the objective function (e.g. data samples for machine learning task) being distributed over n machines that can only communicate to their neighbors on a fixed communication graph. To reduce the communi ...

2019