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arXiv · 1105.2274

Data-Distributed Weighted Majority and Online Mirror Descent

Hua Ouyang·Alexander Gray
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Abstract

In this paper, we focus on the question of the extent to which online learning can benefit from distributed computing. We focus on the setting in which $N$ agents online-learn cooperatively, where each agent only has access to its own data. We propose a generic data-distributed online learning meta-algorithm. We then introduce the Distributed Weighted Majority and Distributed Online Mirror Descent algorithms, as special cases. We show, using both theoretical analysis and experiments, that compared to a single agent: given the same computation time, these distributed algorithms achieve smaller generalization errors; and given the same generalization errors, they can be $N$ times faster.

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Hua Ouyang, Alexander Gray. 2011-05-11. Data-Distributed Weighted Majority and Online Mirror Descent. https://arxiv.org/abs/1105.2274

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