用联邦学习实现跨领域数据协作，保护隐私同时保持模型高效
Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration
- 在客户端本地训练模型，只共享参数不传原始数据
- 医疗、金融等多源数据下模型性能高且隐私保护有效
- 适合需跨机构合作又严守数据隐私的场景
本文提出一种基于联邦学习的数据隐私保护框架，旨在实现跨域数据协作的同时保障数据隐私。联邦学习通过在各客户端本地训练模型并仅共享参数而非原始数据，显著降低隐私泄露风险。实验通过模拟医疗、金融及用户数据，在不同数据源下验证了其高效性与隐私保护能力。结果表明，联邦学习在多领域环境下既能维持高模型性能，又能有效保护数据隐私。该研究为跨域数据协作提供了新技术路径，推动大规模数据分析与机器学习在隐私保护前提下的应用。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。