<ul><li>This paper introduces a communication-efficient penalized regression algorithm for high-dimensional sparse linear regression models with massive data.</li><li>The algorithm, named CESDAR, leverages an optimized distributed system communication algorithm and introduces the communication-efficient surrogate likelihood framework to enhance privacy and data security.</li><li>It achieves the same statistical accuracy as the global estimator while significantly reducing communication costs.</li><li>Simulations and real data benchmarks experiments demonstrate the efficiency and accuracy of the CESDAR algorithm.</li></ul>

Communication-Efficient l_0 Penalized Least Square

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