Peyvandi Amirhossein, Majidi Babak, Peyvandi Soodeh, Patra Jagdish C
Department of Computer Engineering, Khatam University, Tehran, Iran.
Emergency and Rapid Response Simulation (ADERSIM) Artificial Intelligence Group, Faculty of Liberal Arts & Professional Studies, York University, Toronto, Canada.
Multimed Tools Appl. 2022;81(18):25029-25050. doi: 10.1007/s11042-022-12900-5. Epub 2022 Mar 22.
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.
训练诸如深度学习之类的监督式机器学习模型需要高质量的标记数据集，这些数据集要包含来自各类别和特定案例的足够样本。数据即服务（DaaS）可为训练高效的机器学习模型提供此类高质量数据。然而，隐私问题可能会降低数据所有者参与DaaS供应的积极性。本文提出了一种基于区块链的去中心化联邦学习框架，用于实现安全、可扩展且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、数据平等性和计算智能平等性。该框架利用区块链网络在云端安全地进行数据和机器学习模型的去中心化传输与共享。作为多媒体应用的案例研究，分析了DCIaaS框架在生物医学图像分类和有害垃圾管理方面的性能。实验结果表明，与去中心化训练相比，使用所提出框架训练的模型准确率有所提高。所提出的框架利用分布式账本技术解决了DaaS中的隐私保护问题，并作为一个众包机器学习模型训练过程的平台。
