Blockchain Based Global Financial Service Platform

Mingyang Zhang, Yingjun Li, Chonghe Zheng, Xu Han, Haisong Gu, Heping Pan
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Abstract

Recently AI technologies, especially Deep Neural Network (DNN), have been widely used in the financial industry, such as stock price and movement prediction. In order to develop an AI-based solution for the global financial market, daily-based DNN model training for each stock is required to need collaboration among a large scale of entities. However, it is usually challenging due to data privacy, the cost of AI computing, and the lack of motivation to share information. This research proposes a novel Blockchain based platform, which utilizes the decentralized network, federated learning, and master-node to tackle these issues. The decentralized computing framework of federated learning, along with transfer learning, is applied to meet the data privacy requirements. Furthermore, the proposed federated learning platform with collaborative training is built on a decentralized AI computing cloud, which is highly affordable compared to centralized AI clouds. The master-node of Blockchain technology is further employed to enable the scalable global financial service, and effective rewards are applied to incentivize information sharing as well. We have applied the proposed Blockchain based platform to the stock prediction global service, which demonstrates the platform is practical and useful.
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基于区块链的全球金融服务平台
近年来,人工智能技术,特别是深度神经网络(Deep Neural Network, DNN)已广泛应用于金融行业,如股票价格和走势预测。为了为全球金融市场开发基于人工智能的解决方案,需要在大规模的实体之间进行协作,对每个股票进行基于日常的DNN模型训练。然而,由于数据隐私、人工智能计算的成本以及缺乏共享信息的动机,这通常是具有挑战性的。本研究提出了一种新颖的基于区块链的平台,利用去中心化网络、联邦学习和主节点来解决这些问题。采用联邦学习的分散计算框架和迁移学习来满足数据隐私要求。此外,所提出的具有协同训练的联邦学习平台建立在一个分散的人工智能计算云上,与集中式人工智能云相比,这是非常经济实惠的。进一步利用区块链技术的主节点来实现可扩展的全球金融服务,并采用有效的奖励来激励信息共享。我们将提出的基于区块链的平台应用于股票预测全球服务,证明了该平台的实用性和实用性。
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