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

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.

High performance of privacy-preserving acute myocardial infarction auxiliary diagnosis based on federated learning: a multicenter retrospective study

“…The machine learning algorithms that can be used in FL include neural networks, which were used in the current study, logistic regression, and a tree-based model ( 19 , 20 ), which is suitable for complex iterative computing scenarios of big data modeling and predictive analysis. The technical characteristics of FL include the following: (I) the data of all parties shall be kept locally without disclosing privacy or violating laws and regulations; (II) under the FL system, each participant has the same identity and status; (III) the modeling effect of FL is the same as that of modeling the whole dataset in 1 place, or there is little difference; and (IV) all participants combine data to establish a virtual common model and a system for mutual benefit ( 21 ).…”

The evolution of data pricing: From economics to computational intelligence

“…Query-based pricing is also considered from a privacy perspective [ 55 ]. With the development of big data technology, this class also includes artificial intelligence-related approaches, such as federated learning [ 56 ], deep learning [ 57 ], and other approaches to data pricing. Additionally, there are online pricing platforms [ 58 ], dynamic pricing [ 59 ] and other methods.…”

“…These are the current research hotspots in this field. For instance, Peyvandi et al [ 56 ] suggested a distributed federated learning architecture based on blockchain for safe, scalable, and privacy-preserving smart data pricing. The Stackelberg game is Cluster #9, where researchers primarily concentrate on economic pricing theories, including the data market, auction, and data economics.…”

Demoethical Model of Sustainable Development of Society: A Roadmap towards Digital Transformation

“…Allocating responsibility and accountability. As machines and AI systems become increasingly autonomous and influential, issues surrounding responsibility and accountability emerge [52][53][54]. Demoethical frameworks must address the challenges of assigning liability when incidents occur involving intelligent systems.…”