Distributed Approximation of Functions over Fast Fading Channels with Applications to Distributed Learning and the Max-Consensus Problem
Distributed Approximation of Functions over Fast Fading Channels with Applications to Distributed Learning and the Max-Consensus Problem
复制标题
快衰落通道上函数的分布式逼近及其在分布式学习和最大共识问题中的应用
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
S. Stańczak
中科院分区：
文献类型：
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作者：
I. Bjelakovic;M. Frey;S. Stańczak
在这项工作中，我们考虑的问题，分布逼近的功能在多址信道加性噪声。与以前的作品相比，我们考虑到快速衰落，并给出明确的概率界的近似误差，使我们能够推导出的信道使用的数量上的界限，需要近似一个函数到一个给定的近似精度。衰落和噪声过程都不限于高斯分布。相反，我们考虑亚高斯随机变量，其中包括高斯以及许多其他分布的实际意义。结果的动机，并立即应用到分布式机器学习模型中的预测器的计算和B）超密集网络中的最大共识问题。
In this work, we consider the problem of distributed approximation of functions over multiple-access channels with additive noise. In contrast to previous works, we take fast fading into account and give explicit probability bounds for the approximation error allowing us to derive bounds on the number of channel uses that are needed to approximate a function up to a given approximation accuracy. Neither the fading nor the noise process is limited to Gaussian distributions. Instead, we consider sub-gaussian random variables which include Gaussian as well as many other distributions of practical relevance. The results are motivated by and have immediate applications to a computing predictors in models for distributed machine learning and b) the max-consensus problem in ultradense networks.
DOI：
10.1109/icc.2016.7510770
发表时间：
2015-12
期刊：
2016 IEEE International Conference on Communications (ICC)
影响因子：
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作者：
Kiril Ralinovski;Mario Goldenbaum;S. Stańczak
通讯作者：
Kiril Ralinovski;Mario Goldenbaum;S. Stańczak