通信效率和保护隐私的可验证聚合为联合学习的联合学习
Kaixin Peng1, Xiaoying Shen2,3, Le Gao1
1The Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China.
Entropy (Basel, Switzerland)
|August 26, 2023
概括
本研究介绍了一种安全的联合学习 (FL) 方案,以保护用户隐私和聚合完整性. 它使用渐变加密和散列来防止数据推断和验证结果,提高FL的可靠性.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 密码学 密码学 密码学 密码学
背景情况:
- 联合学习 (FL) 允许在不共享原始数据的情况下进行分散的模型培训.
- 在FL中不可信任的服务器存在私人信息推断和结果操纵的风险.
- 确保隐私和聚合完整性对于可靠的FL部署至关重要.
研究的目的:
- 提出一个安全和可验证的联合学习计划.
- 为了加强对恶意服务器的隐私保护.
- 确保聚合模型结果的完整性.
主要方法:
- 实施了一个具有安全梯度聚合的联合学习方案.
- 使用单面罩技术来加密梯度以保护用户数据.
- 在掩盖梯度上使用哈希,以验证聚合完整性.
主要成果:
- 拟议的方案有效地阻止恶意服务器推断私人用户信息.
- 通过哈希机制验证聚合结果的完整性.
- 证明了高通信效率,适用于带宽受限制的环境.
结论:
- 开发的安全聚合可验证的联合学习方案提供了强大的隐私和完整性.
- 该协议对于带宽有限和离线用户的场景特别有效.
- 这种方法提高了联合学习系统的安全性和可靠性.
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