Mobility

Cooperative Training for Attribute-Distributed Data: Trade-Off Between Data Transmission and Performance

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Executive Summary

This paper introduces a modeling frame-work for distributed regression with agents/experts observing attribute-distributed data (heterogeneous data). Under this model, a new algorithm, the Iterative Covariance Optimization Algorithm (ICOA), is designed to reshape the covariance matrix of the training residuals of individual agents so that the linear combination of the individual estimators minimizes the ensemble training error. Moreover, a scheme (Minimax Protection) is designed to provide a trade-off between the number of data instances transmitted among the agents and the performance of the ensemble estimator without undermining the convergence of the algorithm.

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