Privacy-preserving federated learning for scalable and high data quality computational-intelligence-as-a-service in Society 5.0

Amirhossein Peyvandi, Babak Majidi, Soodeh Peyvandi, Jagdish Patra

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)25029-25050
Number of pages22
JournalMultimedia Tools and Applications
Volume81
Early online date22 Mar 2022
DOIs
Publication statusPublished - 22 Mar 2022

Keywords

  • Blockchain
  • Data as a service
  • Decentralized machine learning
  • Federated learning
  • Privacy preserving
  • Society 5.0

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