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Decentralized AdaBoost algorithm over sensor networks
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DOI：10.1016/j.neucom.2022.01.015.png)
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Abstract
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In this paper, we study the decentralized AdaBoost problem over sensor networks, and propose a fully decentralized AdaBoost algorithm, where each sensor can obtain the centralized global solution without transmission of private dataset. By decomposing the centralized cost function into a summation of local ones, we convert decentralized AdaBoost problem into a distributed optimization problem, and design a distributed alternating minimization method to solve it. In order to improve convergence rate, motivated by Nesterov gradient descent method, we propose a fast decentralized AdaBoost algorithm. Then, we prove the convergence of proposed algorithms. Moreover, we deduce decentralized AdaBoost algorithm for logistic regression in detail. The simulations with Spam-Email dataset illustrate the effectiveness of proposed algorithms. (c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Sensor networks
Decentralized AdaBoost
Distributed optimization
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