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Empirical risk minimization is perhaps the most influential idea instatistical learning, with applications to nearly all scientific and technicaldomains in the form of regression and classification models. To analyze massivestreaming datasets in distributed computing environments, practitionersincreasingly prefer to deploy regression models on edge rather than in thecloud. By keeping data on edge devices, we minimize the energy, communication,and data security risk associated with the model. Although it is equallyadvantageous to train models at the edge, a common assumption is that the modelwas originally trained in the cloud, since training typically requiressubstantial computation and memory. To this end, we propose STORM, an onlinesketch for empirical risk minimization. STORM compresses a data stream into atiny array of integer counters. This sketch is sufficient to estimate a varietyof surrogate losses over the original dataset. We provide rigorous theoreticalanalysis and show that STORM can estimate a carefully chosen surrogate loss forthe least-squares objective. In an exhaustive experimental comparison forlinear regression models on real-world datasets, we find that STORM allowsaccurate regression models to be trained.

STORM: Foundations of End-to-End Empirical Risk Minimization on the Edge