Blockchain-Based Federated Learning: Incentivizing Data Sharing and
  Penalizing Dishonest Behavior
Amir Jaberzadeh Ajay Kumar Shrestha Faijan Ahamad Khan Mohammed Afaan Shaikh Bhargav Dave Jason Geng
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.