A Systematic Survey of Blockchained Federated Learning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2021
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| _version_ | 1866911897087901696 |
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| author | Wang, Zhilin Hu, Qin Xu, Minghui Zhuang, Yan Wang, Yawei Cheng, Xiuzhen |
| author_facet | Wang, Zhilin Hu, Qin Xu, Minghui Zhuang, Yan Wang, Yawei Cheng, Xiuzhen |
| contents | With the technological advances in machine learning, effective ways are available to process the huge amount of data generated in real life. However, issues of privacy and scalability will constrain the development of machine learning. Federated learning (FL) can prevent privacy leakage by assigning training tasks to multiple clients, thus separating the central server from the local devices. However, FL still suffers from shortcomings such as single-point-failure and malicious data. The emergence of blockchain provides a secure and efficient solution for the deployment of FL. In this paper, we conduct a comprehensive survey of the literature on blockchained FL (BCFL). First, we investigate how blockchain can be applied to federal learning from the perspective of system composition. Then, we analyze the concrete functions of BCFL from the perspective of mechanism design and illustrate what problems blockchain addresses specifically for FL. We also survey the applications of BCFL in reality. Finally, we discuss some challenges and future research directions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2110_02182 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | A Systematic Survey of Blockchained Federated Learning Wang, Zhilin Hu, Qin Xu, Minghui Zhuang, Yan Wang, Yawei Cheng, Xiuzhen Cryptography and Security Artificial Intelligence With the technological advances in machine learning, effective ways are available to process the huge amount of data generated in real life. However, issues of privacy and scalability will constrain the development of machine learning. Federated learning (FL) can prevent privacy leakage by assigning training tasks to multiple clients, thus separating the central server from the local devices. However, FL still suffers from shortcomings such as single-point-failure and malicious data. The emergence of blockchain provides a secure and efficient solution for the deployment of FL. In this paper, we conduct a comprehensive survey of the literature on blockchained FL (BCFL). First, we investigate how blockchain can be applied to federal learning from the perspective of system composition. Then, we analyze the concrete functions of BCFL from the perspective of mechanism design and illustrate what problems blockchain addresses specifically for FL. We also survey the applications of BCFL in reality. Finally, we discuss some challenges and future research directions. |
| title | A Systematic Survey of Blockchained Federated Learning |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2110.02182 |