iFLBC: On the Edge Intelligence Using Federated Learning Blockchain Network

【Author】 Doku, Ronald; Rawat, Danda B.

【Source】2020 IEEE 6TH INT CONFERENCE ON BIG DATA SECURITY ON CLOUD (BIGDATASECURITY) / 6TH IEEE INT CONFERENCE ON HIGH PERFORMANCE AND SMART COMPUTING, (HPSC) / 5TH IEEE INT CONFERENCE ON INTELLIGENT DATA AND SECURITY (IDS)

【Abstract】Lately there has been an increase in the number of Machine Learning (ML) and Artificial Intelligence (AI) applications ranging from recommendation systems to face to speech recognition. At the helm of the advent of deep learning is the proliferation of data from diverse data sources ranging from Internet-of-Things (IoT) devices to self-driving automobiles. Tapping into this unlimited reservoir of information presents the problem of finding quality data out of a myriad of irrelevant ones, which to this day, has been a significant issue in data science with a direct ramification of this being the inability to generate quality ML models for useful predictive analysis. Edge computing has been deemed a solution to some of issues such as privacy, security, data silos and latency, as it ventures to bring cloud computing services closer to end-nodes. A new form of edge computing known as edge-AI attempts to bring ML, AI, and predictive analytics services closer to the data source (end devices). In this paper, we investigate an approach to bring edge-AI to end-nodes through a shared machine learning model powered by the blockchain technology and a federated learning framework called iFLBC edge. Our approach addresses the issue of the scarcity of relevant data by devising a mechanism known as the Proof of Common Interest (PoCI) to sieve out relevant data from irrelevant ones. The relevant data is trained on a model, which is then aggregated along with other models to generate a shared model that is stored on the blockchain. The aggregated model is downloaded by members of the network which they can utilize for the provision of edge intelligence to end-users. This way, AI can be more ubiquitous as members of the iFLBC network can provide intelligence services to end-users.

【Keywords】

【标题】iFLBC：使用联邦学习区块链网络的边缘智能

【摘要】最近，机器学习 (ML) 和人工智能 (AI) 应用的数量有所增加，从推荐系统到人脸再到语音识别。引领深度学习出现的是来自各种数据源的数据激增，从物联网 (IoT) 设备到自动驾驶汽车。利用这个无限的信息库提出了从无数不相关的数据中找到高质量数据的问题，直到今天，这一直是数据科学中的一个重要问题，其直接后果是无法生成高质量的机器学习模型有用的预测分析。边缘计算被认为是解决隐私、安全、数据孤岛和延迟等一些问题的解决方案，因为它冒险使云计算服务更接近终端节点。一种称为边缘人工智能的新形式的边缘计算试图让机器学习、人工智能和预测分析服务更接近数据源（终端设备）。在本文中，我们研究了一种通过由区块链技术支持的共享机器学习模型和称为 iFLBC 边缘的联邦学习框架将边缘 AI 引入终端节点的方法。我们的方法通过设计一种称为共同利益证明 (PoCI) 的机制来解决相关数据稀缺的问题，以从不相关的数据中筛选出相关数据。相关数据在模型上进行训练，然后与其他模型一起聚合以生成存储在区块链上的共享模型。聚合模型由网络成员下载，他们可以利用这些模型向最终用户提供边缘智能。通过这种方式，人工智能可以更加普遍，因为 iFLBC 网络的成员可以为最终用户提供情报服务。

【关键词】无

【发表时间】2020

【收录时间】2022-07-06

【文献类型】Proceedings Paper

【论文大主题】区块链联邦学习

【论文小主题】联邦学习为主体

【翻译者】石东瑛