Section: New Results

Decentralized Topic Modelling with Latent Dirichlet Allocation

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 a master or 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.

In [14], we tackle the non-convex problem of topic modelling, where agents have sensitive text data at their disposal that they can not or do not want to share (e.g., text messages, emails, confidential reports). More precisely, we adapt the particular Latent Dirichlet Allocation (LDA) model to decentralized networks. We combine recent work of [22] on online inference for latent variable models, which adapts online EM with local Gibbs sampling in the case of intractable latent variable models (such as LDA) and recent advances on decentralized optimization.