可信和自动化的机器学习运行区块链及其应用
Trustable and Automated Machine Learning Running with Blockchain and Its Applications
摘要：
机器学习算法从数据中学习，并使用来自可变数据库的数据;因此，机器学习的数据和结果不能完全信任。此外，机器学习过程通常难以自动化。可信机器学习的统一分析框架已经在文献中提出。它提出了通过使用区块链技术构建一个可信的机器学习系统，该系统可以以永久和不可变的方式存储数据。此外，区块链上的智能合约用于自动化机器学习过程。在所提出的框架中，核心机器学习算法可以有三种实现：服务器层实现、流传输层实现和智能合约实现。然而，仍有一些悬而未决的问题。首先，流媒体层通常部署在边缘设备上，因此内存和计算能力有限。我们如何在流层上运行机器学习？其次，存储在区块链上的大多数数据都是金融交易，通常需要进行欺诈检测。然而，在某些应用中，训练数据很难获得。我们是否可以构建良好的机器学习模型来使用有限的训练数据进行欺诈检测？这些问题促使本文，这使得两个贡献。首先，它提出在服务器层训练机器学习模型，并使用特殊的二进制数据格式保存模型。然后，流层可以将这个二进制数据块作为输入，并在线对传入的数据进行评分。二进制数据的blob非常紧凑，可以部署在边缘设备上。其次，提出了一种新的合成数据生成方法，该方法可以丰富训练数据集。实验表明，这种合成数据生成在金融数据中的欺诈检测等应用中非常有效。
英文摘要：
Machine learning algorithms learn from data and use data from databases that are mutable; therefore, the data and the results of machine learning cannot be fully trusted. Also, the machine learning process is often difficult to automate. A unified analytical framework for trustable machine learning has been presented in the literature. It proposed building a trustable machine learning system by using blockchain technology, which can store data in a permanent and immutable way. In addition, smart contracts on blockchain are used to automate the machine learning process. In the proposed framework, a core machine learning algorithm can have three implementations: server layer implementation, streaming layer implementation, and smart contract implementation. However, there are still open questions. First, the streaming layer usually deploys on edge devices and therefore has limited memory and computing power. How can we run machine learning on the streaming layer? Second, most data that are stored on blockchain are financial transactions, for which fraud detection is often needed. However, in some applications, training data are hard to obtain. Can we build good machine learning models to do fraud detection with limited training data? These questions motivated this paper; which makes two contributions. First, it proposes training a machine learning model on the server layer and saving the model with a special binary data format. Then, the streaming layer can take this blob of binary data as input and score incoming data online. The blob of binary data is very compact and can be deployed on edge devices. Second, the paper presents a new method of synthetic data generation that can enrich the training data set. Experiments show that this synthetic data generation is very effective in applications such as fraud detection in financial data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
