Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior

TL;DR

This paper introduces a blockchain-based federated learning framework that incentivizes honest data sharing, enhances security, and improves model accuracy through a decentralized platform using smart contracts and blockchain technology.

Contribution

It presents a novel integrated framework combining blockchain, smart contracts, and federated learning to promote secure, fair, and efficient decentralized data sharing and model training.

Findings

Improved accuracy of federated learning models with blockchain integration

Effective incentives and penalties for honest and dishonest behavior

Successful training of CNN on MNIST using the proposed platform

Abstract

With the increasing importance of data sharing for collaboration and innovation, it is becoming more important to ensure that data is managed and shared in a secure and trustworthy manner. Data governance is a common approach to managing data, but it faces many challenges such as data silos, data consistency, privacy, security, and access control. To address these challenges, this paper proposes a comprehensive framework that integrates data trust in federated learning with InterPlanetary File System, blockchain, and smart contracts to facilitate secure and mutually beneficial data sharing while providing incentives, access control mechanisms, and penalizing any dishonest behavior. The experimental results demonstrate that the proposed model is effective in improving the accuracy of federated learning models while ensuring the security and fairness of the data-sharing process. The research paper also presents a decentralized federated learning platform that successfully trained a CNN model on the MNIST dataset using blockchain technology. The platform enables multiple workers to train the model simultaneously while maintaining data privacy and security. The decentralized architecture and use of blockchain technology allow for efficient communication and coordination between workers. This platform has the potential to facilitate decentralized machine learning and support privacy-preserving collaboration in various domains.