Decentralized Learning of Decision Models for Classification with Dependent Agents

Under distributed decision-making (a.k.a. social learning), several agents cooperate according to a communication graph to solve a classification task. A traditional assumption in social learning is that the spatial dependence across the agents’ data is not available, while each individual agent is assumed to know a local model encoding the marginal distribution of its personal data. Recent work shows that, when some joint parametric model is available to describe the spatial dependence, the theory of copulas can be exploited to learn the joint distribution of the data and improve the decision-making performance. However, in most cases a model for the joint distribution is not available. This work addresses this challenging scenario. We propose a fully decentralized strategy where the network of agents reproduces a multilayer perceptron to build a discriminative model capturing the spatial dependence. It is shown that this strategy sensibly outperforms the existing strategies.