Decentralized Topic Modelling with Latent Dirichlet Allocation

Igor Colin (1) , Christophe Dupuy (2, 3)

1 LTCI - Laboratoire Traitement et Communication de l'Information
2 Technicolor R & I [Cesson Sévigné]
3 SIERRA - Statistical Machine Learning and Parsimony

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.

Domaines: Machine Learning [stat.ML]

Igor Colin, Christophe Dupuy. Decentralized Topic Modelling with Latent Dirichlet Allocation. NIPS 2016 - 30th Conference on Neural Information Processing Systems, Dec 2016, Barcelone, Spain. ⟨hal-01383111⟩