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Distributed learning for Random Vector Functional-Link networks

机译：随机向量功能链接网络的分布式学习

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This paper aims to develop distributed learning algorithms for Random Vector Functional-Link (RVFL) networks, where training data is distributed under a decentralized information structure. Two algorithms are proposed by using Decentralized Average Consensus (DAC) and Alternating Direction Method of Multipliers (ADMM) strategies, respectively. These algorithms work in a fully distributed fashion and have no requirement on coordination from a central agent during the learning process. For distributed learning, the goal is to build a common learner model which optimizes the system performance over the whole set of local data. In this work, it is assumed that all stations know the initial weights of the input layer, the output weights of local RVFL networks can be shared through communication channels among neighboring nodes only, and local datasets are blocked strictly. The proposed learning algorithms are evaluated over five benchmark datasets. Experimental results with comparisons show that the DAC-based learning algorithm performs favorably in terms of effectiveness, efficiency and computational complexity, followed by the ADMM-based learning algorithm with promising accuracy but higher computational burden. (c) 2015 Elsevier Inc. All rights reserved.
机译：本文旨在为随机向量功能链接（RVFL）网络开发分布式学习算法，其中训练数据在分散的信息结构下分布。分别采用分散平均共识（DAC）和乘数交替方向方法（ADMM）策略提出了两种算法。这些算法以完全分布式的方式工作，在学习过程中不需要中央代理的协调。对于分布式学习，目标是建立一个通用的学习器模型，该模型在整个本地数据集上优化系统性能。在这项工作中，假设所有站点都知道输入层的初始权重，只能通过相邻节点之间的通信通道共享本地RVFL网络的输出权重，并且严格阻止了本地数据集。在五个基准数据集上评估了所提出的学习算法。对比实验结果表明，基于DAC的学习算法在有效性，效率和计算复杂性方面表现良好，其次是基于ADMM的学习算法，准确性高但计算量大。 （c）2015 Elsevier Inc.保留所有权利。

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