Leveraging Centric Data Federated Learning Using Blockchain For Integrity Assurance

TL;DR

This paper introduces a blockchain-based federated learning framework that creates a virtual marketplace for data and model sharing, improving data quality, integrity, and AI model accuracy.

Contribution

It presents a novel data-centric federated learning architecture utilizing blockchain and smart contracts to enhance data quality and incentivize contributions.

Findings

Increased training dataset size by 100 inputs daily with one user simulation.

Model accuracy improved by approximately 4%.

Enhanced data integrity through blockchain-based incentives.

Abstract

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\%.