Distributed Machine Learning Algorithms: Communication-Computation Trade-offs - Part 2Sundararajan Sellamanickam
This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a dist...
然而,如果需要达到较高的精度,GD或其更快的变体更为适用。 2.2 A Novel Breed of Randomized Algorithms 近年来出现了一系列新的随机方法,这些方法在一阶近似中结合了SGD廉价迭代和GD快速收敛的优点。这些方法大多可以说是属于随机坐标下降变体中的两类对偶方法之一,以及原始的具有方差消减变种的随机梯度下降法。 随机...
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We,rst review previous work on algorithms where the output model is equivalent.Caragea et al. (2004)present a general strategy for transforming machine learning algorithms into distributed learning algorithms.They de-termine conditions for which a distributed approach is better than a central-ized app...
Inspired by approximate consensus, several Byzantine- resilient ML algorithms were proposed yet, all assumed a single correct parameter server: only workers could be Byzantine. Three Median-based aggregation rules were pro- posed to resist Byzantine attacks [65]. Krum [11] and Multi-Krum [17] ...
Survey on machine learning in 5G [34] • Introduction of basic machine learning concepts and algorithms, such as supervised learning, unsupervised learning and reinforcement learning • Introduction of some use cases of applying machine learning to 5G A survey of 5G network systems: challenges and...
Light Gradient Boosting Machine LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages: Faster training speed and higher efficiency. Lower memory usage. ...
First, we propose a functional architecture of federated learning systems and a taxonomy of related techniques. Second, we explain the federated learning systems from four aspects: diverse types of parallelism, aggregation algorithms, data communication, and the security of federated learning systems. ...
accessible from Spark workflows. Spark users can select the best features from either platform to meet their Machine Learning needs. Users can combine Spark's RDD API and Spark MLLib with H2O’s machine learning algorithms, or use H2O independently of Spark for the model building process and po...