Learning from Heterogeneous Data Based on Social Interactions over Graphs

TL;DR

This paper introduces a decentralized, data-driven social machine learning framework enabling agents to collaboratively classify streaming data over a graph without prior distribution knowledge, improving continual learning.

Contribution

It presents a novel social machine learning strategy that combines training and prediction phases, allowing agents to learn from streaming data and adapt over time without prior distribution assumptions.

Findings

Agents learn consistently in heterogeneous settings

Network continues learning during prediction phase

Performance improves over time with ongoing data exchange

Abstract

This work proposes a decentralized architecture, where individual agents aim at solving a classification problem while observing streaming features of different dimensions and arising from possibly different distributions. In the context of social learning, several useful strategies have been developed, which solve decision making problems through local cooperation across distributed agents and allow them to learn from streaming data. However, traditional social learning strategies rely on the fundamental assumption that each agent has significant prior knowledge of the underlying distribution of the observations. In this work we overcome this issue by introducing a machine learning framework that exploits social interactions over a graph, leading to a fully data-driven solution to the distributed classification problem. In the proposed social machine learning (SML) strategy, two phases are present: in the training phase, classifiers are independently trained to generate a belief over a set of hypotheses using a finite number of training samples; in the prediction phase, classifiers evaluate streaming unlabeled observations and share their instantaneous beliefs with neighboring classifiers. We show that the SML strategy enables the agents to learn consistently under this highly-heterogeneous setting and allows the network to continue learning even during the prediction phase when it is deciding on unlabeled samples. The prediction decisions are used to continually improve performance thereafter in a manner that is markedly different from most existing static classification schemes where, following training, the decisions on unlabeled data are not re-used to improve future performance.