DP-BREM:与客户势头相结合的差别私有和强大的联邦学习
Xiaolan Gu1, Ming Li1, Li Xiong2
1University of Arizona.
概括
本研究介绍了DP-BREM和DP-BREM+,新的联合学习 (FL) 协议,同时确保差异隐私 (DP) 和拜占庭稳定性. 这些方法利用客户端的动力和安全的聚合来提高协作机器学习中的安全性和性能.
科学领域:
- 机器学习 机器学习
- 网络安全 网络安全
- 分布式系统 分布式系统
背景情况:
- 联合学习 (FL) 能够实现协作模式培训,但面临隐私和稳定性攻击的脆弱性.
- 现有的防御通常优先考虑隐私或强度,而不是两者兼而有之.
- 跨 silo FL 设置需要针对复杂威胁的强大解决方案.
研究的目的:
- 开发联合学习协议,同时实现差异隐私 (DP) 和拜占庭强度.
- 解决现有的FL防御系统的局限性,该系统无法提供隐私和稳定性保证的结合.
- 提出能够抵御跨 silo FL 环境中的攻击的方法.
主要方法:
- 引入了客户端势头,以随着时间的推移平均更新,增强对拜占庭式攻击的稳定性.
- 开发了DP-BREM,通过将噪音添加到聚合动力并计算其隐私成本来应用DP.
- 拟议的DP-BREM+使用安全聚合来实现分散的DP噪声生成,消除了对可信服务器的需求.
主要成果:
- 与基线方法相比,DP-BREM和DP-BREM+显示出优越的隐私-实用性权衡.
- 拟议的协议在各种攻击场景下展示了增强的拜占庭强度.
- 理论分析和实验验证开发方法的有效性.
结论:
- 开发的DP-BREM和DP-BREM+协议为安全和私有联合学习提供了强大的解决方案.
- 客户端势头和安全聚合是实现同时DP和拜占庭强度的有效技术.
- 这些发现推动了安全可靠的分布式机器学习领域的发展.
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