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This work proposes a decentralized architecture, where individual agents aimat solving a classification problem while observing streaming features ofdifferent dimensions and arising from possibly different distributions. In thecontext of social learning, several useful strategies have been developed,which solve decision making problems through local cooperation acrossdistributed agents and allow them to learn from streaming data. However,traditional social learning strategies rely on the fundamental assumption thateach agent has significant prior knowledge of the underlying distribution ofthe observations. In this work we overcome this issue by introducing a machinelearning framework that exploits social interactions over a graph, leading to afully data-driven solution to the distributed classification problem. In theproposed social machine learning (SML) strategy, two phases are present: in thetraining phase, classifiers are independently trained to generate a belief overa set of hypotheses using a finite number of training samples; in theprediction phase, classifiers evaluate streaming unlabeled observations andshare their instantaneous beliefs with neighboring classifiers. We show thatthe SML strategy enables the agents to learn consistently under thishighly-heterogeneous setting and allows the network to continue learning evenduring the prediction phase when it is deciding on unlabeled samples. Theprediction decisions are used to continually improve performance thereafter ina manner that is markedly different from most existing static classificationschemes where, following training, the decisions on unlabeled data are notre-used to improve future performance.

Learning from Heterogeneous Data Based on Social Interactions over Graphs