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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Fully Decentralized Semi-supervised Learning via Privacy-preserving Matrix Completion
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Fully Decentralized Semi-supervised Learning via Privacy-preserving Matrix Completion

机译：通过隐私保护矩阵完成的完全去中心化半监督学习

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摘要

Distributed learning refers to the problem of inferring a function when the training data are distributed among different nodes. While significant work has been done in the contexts of supervised and unsupervised learning, the intermediate case of Semi-supervised learning in the distributed setting has received less attention. In this paper, we propose an algorithm for this class of problems, by extending the framework of manifold regularization. The main component of the proposed algorithm consists of a fully distributed computation of the adjacency matrix of the training patterns. To this end, we propose a novel algorithm for low-rank distributed matrix completion, based on the framework of diffusion adaptation. Overall, the distributed Semi-supervised algorithm is efficient and scalable, and it can preserve privacy by the inclusion of flexible privacy-preserving mechanisms for similarity computation. The experimental results and comparison on a wide range of standard Semi-supervised benchmarks validate our proposal.
机译：分布式学习是指当训练数据分布在不同节点之间时推断功能的问题。尽管在有监督和无监督学习的背景下已经进行了大量工作，但在分布式环境中半监督学习的中间案例却受到了较少的关注。在本文中，我们通过扩展流形正则化的框架，针对此类问题提出了一种算法。所提出算法的主要组成部分包括训练模式邻接矩阵的完全分布式计算。为此，我们提出了一种基于扩散自适应框架的低秩分布式矩阵完成算法。总体而言，分布式半监督算法高效且可扩展，并且可以通过包含用于相似度计算的灵活的隐私保护机制来保护隐私。实验结果和各种标准半监督基准的比较证实了我们的建议。

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