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| Format: | Preprint |
| Veröffentlicht: |
2023
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| Online-Zugang: | https://arxiv.org/abs/2307.10492 |
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| _version_ | 1866909871695200256 |
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| author | Jaberzadeh, Amir Shrestha, Ajay Kumar Khan, Faijan Ahamad Shaikh, Mohammed Afaan Dave, Bhargav Geng, Jason |
| author_facet | Jaberzadeh, Amir Shrestha, Ajay Kumar Khan, Faijan Ahamad Shaikh, Mohammed Afaan Dave, Bhargav Geng, Jason |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_10492 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior Jaberzadeh, Amir Shrestha, Ajay Kumar Khan, Faijan Ahamad Shaikh, Mohammed Afaan Dave, Bhargav Geng, Jason Machine Learning 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. |
| title | Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2307.10492 |