Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914817762131968 |
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| author | Zuo, Xuhan Wang, Minghao Zhu, Tianqing Zhang, Lefeng Yu, Shui Zhou, Wanlei |
| author_facet | Zuo, Xuhan Wang, Minghao Zhu, Tianqing Zhang, Lefeng Yu, Shui Zhou, Wanlei |
| contents | With the growing need to comply with privacy regulations and respond to user data deletion requests, integrating machine unlearning into IoT-based federated learning has become imperative. Traditional unlearning methods, however, often lack verifiable mechanisms, leading to challenges in establishing trust. This paper delves into the innovative integration of blockchain technology with federated learning to surmount these obstacles. Blockchain fortifies the unlearning process through its inherent qualities of immutability, transparency, and robust security. It facilitates verifiable certification, harmonizes security with privacy, and sustains system efficiency. We introduce a framework that melds blockchain with federated learning, thereby ensuring an immutable record of unlearning requests and actions. This strategy not only bolsters the trustworthiness and integrity of the federated learning model but also adeptly addresses efficiency and security challenges typical in IoT environments. Our key contributions encompass a certification mechanism for the unlearning process, the enhancement of data security and privacy, and the optimization of data management to ensure system responsiveness in IoT scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_20776 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach Zuo, Xuhan Wang, Minghao Zhu, Tianqing Zhang, Lefeng Yu, Shui Zhou, Wanlei Cryptography and Security Artificial Intelligence Distributed, Parallel, and Cluster Computing Machine Learning With the growing need to comply with privacy regulations and respond to user data deletion requests, integrating machine unlearning into IoT-based federated learning has become imperative. Traditional unlearning methods, however, often lack verifiable mechanisms, leading to challenges in establishing trust. This paper delves into the innovative integration of blockchain technology with federated learning to surmount these obstacles. Blockchain fortifies the unlearning process through its inherent qualities of immutability, transparency, and robust security. It facilitates verifiable certification, harmonizes security with privacy, and sustains system efficiency. We introduce a framework that melds blockchain with federated learning, thereby ensuring an immutable record of unlearning requests and actions. This strategy not only bolsters the trustworthiness and integrity of the federated learning model but also adeptly addresses efficiency and security challenges typical in IoT environments. Our key contributions encompass a certification mechanism for the unlearning process, the enhancement of data security and privacy, and the optimization of data management to ensure system responsiveness in IoT scenarios. |
| title | Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach |
| topic | Cryptography and Security Artificial Intelligence Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2405.20776 |