Decentralized Topic Modelling with Latent Dirichlet Allocation

TL;DR

This paper introduces a decentralized approach to topic modeling using LDA, enabling nodes in privacy-sensitive networks to infer shared topics without central data aggregation, maintaining performance with limited communication.

Contribution

It adapts LDA for decentralized optimization, allowing effective topic inference in privacy-preserving networks with limited communication.

Findings

Similar parameters recovered as centralized methods

Performance comparable to stochastic methods

Effective on synthetic data

Abstract

Privacy preserving networks can be modelled as decentralized networks (e.g., sensors, connected objects, smartphones), where communication between nodes of the network is not controlled by an all-knowing, central node. For this type of networks, the main issue is to gather/learn global information on the network (e.g., by optimizing a global cost function) while keeping the (sensitive) information at each node. In this work, we focus on text information that agents do not want to share (e.g., text messages, emails, confidential reports). We use recent advances on decentralized optimization and topic models to infer topics from a graph with limited communication. We propose a method to adapt latent Dirichlet allocation (LDA) model to decentralized optimization and show on synthetic data that we still recover similar parameters and similar performance at each node than with stochastic methods accessing to the whole information in the graph.