基于雨林模型的联邦学习实现

Mainul Karim, Niloy Kumar Kundu, Dipu Saha, Sarah Kabir, Sumaiya Mim, Dewan Md. Farid
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引用次数: 1

摘要

联邦学习(FL)是机器学习中的一个新概念,它跨多个节点和持有本地训练数据的机器训练预测模型,而不与其他节点共享数据。这也被称为协作学习。在FL中,不是发送训练数据,而是将参数值上传到主节点或服务器。相反,雨林是一个使用决策树(DT)分类器处理大数据的概念。DT是一种流行的机器学习算法,它是一种自上而下的递归分治法。在本文中,我们通过应用Rainforest算法来利用联邦学习的概念,其中使用聚类技术将数据集划分为多个数据子集,并从中构建可扩展的决策树。从每个数据子集中,我们有一个AVC(属性值,类标签)表,它被发送到中心主节点或服务器,通过使用矩阵加法来创建完整的决策树。为了训练模型,我们使用了10个不同的数据集并对所提出的模型进行了评估。实验分析表明,总体性能具有良好的准确度和精密度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementing Federated Learning based on RainForest Model
Federated Learning (FL) is a new concept in machine learning that trains predictive models across multiple nodes and machines holding local training data without sharing that data with other nodes. It’s also known as collaborative learning. In FL, instead of sending the training data, only parameter values are uploaded to the master node or server. On the contrary, Rainforest is a concept for dealing with big data using a decision tree (DT) classifier. DT is one of the popular machine learning algorithms, which is a top-down recursive divide and conquer method. In this paper, we utilized the concept of federated learning by applying the Rainforest algorithm, where datasets are divided into several subsets of data using the clustering technique, from which scalable decision trees are constructed. From each subset of data, we have an AVC (attribute-value, class-label) table, which is sent to the central master node or server to create the full decision tree by using matrix addition. To train the model, we used ten different datasets and evaluated the proposed model. The experimental analysis shows very good accuracy and precision in overall performance.
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