# An Asynchronous Distributed Framework for Large-scale Learning Based on Parameter Exchanges

## TL;DR

This paper introduces an asynchronous distributed learning framework that improves efficiency by allowing machines to update shared parameters independently, demonstrating convergence and effectiveness in matrix factorization and classification tasks.

## Contribution

It presents a novel asynchronous distributed framework for large-scale learning that handles heterogeneous machine loads and proves its convergence.

## Key findings

- Converges reliably under asynchronous updates.
- Effective in matrix factorization for recommender systems.
- Performs well in binary classification tasks.

## Abstract

In many distributed learning problems, the heterogeneous loading of computing machines may harm the overall performance of synchronous strategies. In this paper, we propose an effective asynchronous distributed framework for the minimization of a sum of smooth functions, where each machine performs iterations in parallel on its local function and updates a shared parameter asynchronously. In this way, all machines can continuously work even though they do not have the latest version of the shared parameter. We prove the convergence of the consistency of this general distributed asynchronous method for gradient iterations then show its efficiency on the matrix factorization problem for recommender systems and on binary classification.