Leveraging Centric Data Federated Learning Using Blockchain For Integrity Assurance
This addresses data quality and integrity issues for organizations using AI, though it appears incremental as it builds on existing federated learning and blockchain concepts.
The paper tackles the problem of lacking quality data for AI-model training by proposing a data-centric federated learning architecture using blockchain and smart contracts, which increased model accuracy by approximately 4% in a simulation with one user adding an average of 100 inputs daily.
Machine learning abilities have become a vital component for various solutions across industries, applications, and sectors. Many organizations seek to leverage AI-based solutions across their business services to unlock better efficiency and increase productivity. Problems, however, can arise if there is a lack of quality data for AI-model training, scalability, and maintenance. We propose a data-centric federated learning architecture leveraged by a public blockchain and smart contracts to overcome this significant issue. Our proposed solution provides a virtual public marketplace where developers, data scientists, and AI-engineer can publish their models and collaboratively create and access quality data for training. We enhance data quality and integrity through an incentive mechanism that rewards contributors for data contribution and verification. Those combined with the proposed framework helped increase with only one user simulation the training dataset with an average of 100 input daily and the model accuracy by approximately 4\%.