基于边缘情报的联合学习的两阶段差异性隐私计划
IEEE journal of biomedical and health informatics
|August 18, 2023
概括
本研究介绍了两阶段的差异性隐私 (DP) 框架,用于使用边缘智能进行联合学习 (FL). 该方法在分布式学习中增强数据隐私保护,而不会影响模型的准确性或融合.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据安全 数据安全
背景情况:
- 联合学习 (FL) 可以在不共享原始数据的情况下进行协作模式培训,但保护敏感信息仍然是一个挑战.
- 边缘智能提供分布式处理能力,对于保护隐私的FL框架至关重要.
- 现有的FL方法可能无法充分解决数据隐私问题,需要先进的保护机制.
研究的目的:
- 提出一个新的两阶段差异性隐私 (DP) 框架,用于与边缘智能集成的联合学习 (FL).
- 根据数据的敏感性来实现可调节的隐私保护水平.
- 在分布式学习环境中确保数据隐私和模型安全.
主要方法:
- 实施了两阶段的DP框架:使用随机响应和边缘服务器本地模型噪声添加的用户终端特征干扰.
- 利用终端云架构进行联合学习.
- 采用双向长期短期记忆 (BiLSTM) 神经网络在心电图 (ECG) 数据集上进行分类.
主要成果:
- 拟议的框架通过可调节的干扰和噪音添加,证明了有效的隐私保护.
- 实验结果显示,该框架在ECG数据集上实现了良好的培训准确性和趋同.
- 分析证实了不同隐私预算和参数对模型性能的影响.
结论:
- 开发的两阶段DP框架成功地平衡了隐私保护和联合学习中的模型性能.
- 该框架提供了一种灵活的隐私保护方法,可以适应不同的数据敏感性.
- 这项研究为安全和高效的分布式机器学习提供了强大的解决方案.
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