【Author】
Peyvandi, Amirhossein; Majidi, Babak; Peyvandi, Soodeh; Patra, Jagdish C.
【Source】MULTIMEDIA TOOLS AND APPLICATIONS
【Abstract】Training supervised machine learning models like deep learning requires high-quality labelled datasets that contain enough samples from various categories and specific cases. The Data as a Service (DaaS) can provide this high-quality data for training efficient machine learning models. However, the issue of privacy can minimize the participation of the data owners in DaaS provision. In this paper, a blockchain-based decentralized federated learning framework for secure, scalable, and privacy-preserving computational intelligence, called Decentralized Computational Intelligence as a Service (DCIaaS), is proposed. The proposed framework is able to improve data quality, computational intelligence quality, data equality, and computational intelligence equality for complex machine learning tasks. The proposed framework uses the blockchain network for secure decentralized transfer and sharing of data and machine learning models on the cloud. As a case study for multimedia applications, the performance of DCIaaS framework for biomedical image classification and hazardous litter management is analysed. Experimental results show an increase in the accuracy of the models trained using the proposed framework compared to decentralized training. The proposed framework addresses the issue of privacy-preserving in DaaS using the distributed ledger technology and acts as a platform for crowdsourcing the training process of machine learning models.
【Keywords】Federated learning; Blockchain; Privacy preserving; Decentralized machine learning; Data as a service; Society 5; 0
【标题】在社会 5.0 中为可扩展和高质量数据计算智能即服务的隐私保护联邦学习
【摘要】训练像深度学习这样的监督机器学习模型需要高质量的标记数据集，其中包含来自各种类别和特定案例的足够样本。数据即服务 (DaaS) 可以提供这种高质量的数据来训练高效的机器学习模型。但是，隐私问题可以最大限度地减少数据所有者在 DaaS 提供中的参与。在本文中，提出了一种基于区块链的去中心化联邦学习框架，用于安全、可扩展和保护隐私的计算智能，称为去中心化计算智能即服务 (DCIaaS)。所提出的框架能够提高复杂机器学习任务的数据质量、计算智能质量、数据平等和计算智能平等。提议的框架使用区块链网络在云上安全地分散传输和共享数据和机器学习模型。作为多媒体应用的案例研究，分析了用于生物医学图像分类和危险垃圾管理的 DCIaaS 框架的性能。实验结果表明，与分散训练相比，使用所提出的框架训练的模型的准确性有所提高。所提出的框架使用分布式账本技术解决了 DaaS 中的隐私保护问题，并作为众包机器学习模型训练过程的平台。
【关键词】联邦学习；区块链；隐私保护；去中心化机器学习；数据即服务；社会 5； 0
【文献类型】Article; Early Access
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