Skip to Main content Skip to Navigation
Conference papers

Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions

Igor Colin 1 Aurélien Bellet 2 Joseph Salmon 1 Stéphan Clémençon 1
2 MAGNET - Machine Learning in Information Networks
CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189, Inria Lille - Nord Europe
Abstract : In decentralized networks (of sensors, connected objects, etc.), there is an important need for efficient algorithms to optimize a global cost function , for instance to learn a global model from the local data collected by each computing unit. In this paper, we address the problem of decentralized minimization of pairwise functions of the data points, where these points are distributed over the nodes of a graph defining the communication topology of the network. This general problem finds applications in ranking, distance metric learning and graph inference, among others. We propose new gossip algorithms based on dual averaging which aims at solving such problems both in synchronous and asynchronous settings. The proposed framework is flexible enough to deal with constrained and regularized variants of the optimization problem. Our theoretical analysis reveals that the proposed algorithms preserve the convergence rate of centralized dual averaging up to an additive bias term. We present numerical simulations on Area Under the ROC Curve (AUC) maximization and metric learning problems which illustrate the practical interest of our approach.
Complete list of metadata

Cited literature [27 references]  Display  Hide  Download
Contributor : Aurélien Bellet Connect in order to contact the contributor
Submitted on : Thursday, June 9, 2016 - 9:10:37 AM
Last modification on : Thursday, January 20, 2022 - 4:17:12 PM


  • HAL Id : hal-01329315, version 1


Igor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon. Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions. International Conference on Machine Learning (ICML 2016), Jun 2016, New York, United States. ⟨hal-01329315⟩



Les métriques sont temporairement indisponibles