Secure exchange and incentivised collaboration of machine learning models and datasets using blockchain
by Jessica Judith D'souza; R. Padmavathy; Abhinav Dayal; Harika Gnanaveni; Sreenu Ponnada
International Journal of Information and Computer Security (IJICS), Vol. 19, No. 3/4, 2022

Abstract: Machine learning is an extremely centralised platform and foments a skewed dynamic in which only a few people can utilise the enormous opportunities it provides. There is an absolute necessity to build a decentralised platform where the users may exchange the trained models and datasets to solve machine learning problems collaboratively. In this paper, a secure way of exchanging these using a distributed trustless scalable platform through blockchain is proposed, thereby making the entire process seamless instead of relying on the traditional centralised platforms. Further, the performance is improved compared with other platforms by using two approaches such as parallelisation techniques and reducing gas usage. An authentication layer is added to improve the security of the whole system. The proposed design additionally incorporates an incentive mechanism to encourage more individuals to learn machine learning to contribute and hence, drawing them towards collaborative learning.

Online publication date: Wed, 23-Nov-2022

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