[论文解读] Trustable and Automated Machine Learning Running with Blockchain and Its Applications

Tao Wang, WU Xin-min+1 more|arXiv (Cornell University)|Aug 14, 2019

Blockchain Technology Applications and Security参考文献 24被引用 6

一句话总结

本文提出了一种集成区块链的框架，用于可信赖且自动化的机器学习，实现不可篡改的数据存储和智能合约驱动的工作流。该框架引入了一种适用于边缘部署的紧凑二进制模型格式，以及一种合成数据生成方法，显著提升了在真实训练数据有限情况下的欺诈检测性能，在KDD 2019 AutoML工作坊中得到验证。

ABSTRACT

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.

研究动机与目标

• 解决由于数据可变性和工作流复杂性导致的机器学习中缺乏信任和自动化的问题。
• 通过区块链技术实现在资源受限的边缘设备上高效、安全且可审计的机器学习。
• 通过生成高质量的合成数据，克服欺诈检测中真实训练数据有限的挑战。
• 设计一个统一系统，其中模型在中心端训练，经压缩后通过智能合约在边缘设备上执行。

提出的方法

• 在服务器层使用真实数据训练机器学习模型，并将其导出为紧凑的二进制模型数据块。
• 将二进制模型部署到边缘设备（流处理层），以对传入的数据流进行实时推理。
• 使用区块链智能合约编排机器学习流程，确保可复现性和可审计性。
• 实施一种合成数据生成技术，以扩充稀疏的训练数据集，尤其适用于欺诈检测。
• 将模型权重和训练元数据存储在区块链上，以确保不可篡改性和可信度。
• 将三层架构——服务器层、流处理层和智能合约层——整合为一个统一且可验证的机器学习流程。

实验结果

研究问题

• RQ1 如何在内存和计算资源受限的边缘设备上高效部署机器学习模型，同时保持信任和性能？
• RQ2 在低数据场景（如金融欺诈检测）下，合成数据生成是否能有效提升模型性能？
• RQ3 如何利用区块链技术自动化并保障从数据到推理的整个机器学习生命周期的安全性？
• RQ4 在不牺牲准确率的前提下，压缩和传输训练后模型到边缘设备的最优方法是什么？

主要发现

• 二进制模型格式可实现机器学习模型在边缘设备上的高效部署，内存和计算开销极低。
• 合成数据生成方法显著提升了在真实训练数据有限情况下的欺诈检测任务中的模型性能。
• 区块链与智能合约的集成确保了机器学习流程的端到端可审计性、不可篡改性和可复现性。
• 所提出的框架在边缘设备上实现了高推理准确率，同时通过区块链日志实现了强大的安全与信任保障。
• 在KDD 2019 AutoML工作坊的实验验证了该框架在真实金融欺诈检测场景中的有效性。