Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910900283244544 |
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| author | Zhang, Yiwei Liu, Jie Wang, Jiawei Dai, Lu Guo, Fan Cai, Guohui |
| author_facet | Zhang, Yiwei Liu, Jie Wang, Jiawei Dai, Lu Guo, Fan Cai, Guohui |
| contents | This paper proposes a data privacy protection framework based on federated learning, which aims to realize effective cross-domain data collaboration under the premise of ensuring data privacy through distributed learning. Federated learning greatly reduces the risk of privacy breaches by training the model locally on each client and sharing only model parameters rather than raw data. The experiment verifies the high efficiency and privacy protection ability of federated learning under different data sources through the simulation of medical, financial, and user data. The results show that federated learning can not only maintain high model performance in a multi-domain data environment but also ensure effective protection of data privacy. The research in this paper provides a new technical path for cross-domain data collaboration and promotes the application of large-scale data analysis and machine learning while protecting privacy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00282 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration Zhang, Yiwei Liu, Jie Wang, Jiawei Dai, Lu Guo, Fan Cai, Guohui Machine Learning Cryptography and Security This paper proposes a data privacy protection framework based on federated learning, which aims to realize effective cross-domain data collaboration under the premise of ensuring data privacy through distributed learning. Federated learning greatly reduces the risk of privacy breaches by training the model locally on each client and sharing only model parameters rather than raw data. The experiment verifies the high efficiency and privacy protection ability of federated learning under different data sources through the simulation of medical, financial, and user data. The results show that federated learning can not only maintain high model performance in a multi-domain data environment but also ensure effective protection of data privacy. The research in this paper provides a new technical path for cross-domain data collaboration and promotes the application of large-scale data analysis and machine learning while protecting privacy. |
| title | Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2504.00282 |