| Title |
Implementation framework for a blockchain-based federated learning model for classification problems / |
| Authors |
Mahmood, Zeba ; Jusas, Vacius |
| DOI |
10.3390/sym13071116 |
| Full Text |
|
| Is Part of |
Symetry.. Basel : MDPI. 2021, vol. 13, iss. 7, art. no. 1116, p. 1-15.. ISSN 2073-8994 |
| Keywords [eng] |
decentralized ledger ; federated learning (FL) ; artificial intelligence (AI) ; machine learning (ML) ; zero-knowledge proofs (ZKPs) |
| Abstract [eng] |
This paper introduces a blockchain-based federated learning (FL) framework with incentives for participating nodes to enhance the accuracy of classification problems. Machine learning technology has been rapidly developed and changed from a global perspective for the past few years. The FL framework is based on the Ethereum blockchain and creates an autonomous ecosystem, where nodes compete to improve the accuracy of classification problems. With privacy being one of the biggest concerns, FL makes use of the blockchain-based approach to ensure privacy and security. Another important technology that underlies the FL framework is zero-knowledge proofs (ZKPs), which ensure that data uploaded to the network are accurate and private. Basically, ZKPs allow nodes to compete fairly by only submitting accurate models to the parameter server and get rewarded for that. We have conducted an analysis and found that ZKPs can help improve the accuracy of models submitted to the parameter server and facilitate the honest participation of all nodes in FL. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2021 |
| CC license |
|
