联合学习中的安全,隐私和公平的联系:新的平衡和新的视角
Linlin Wang1, Tianqing Zhu1, Wanlei Zhou1
1City University of Macau, Avenida Padre Tomás Pereira Taipa, China.
概括
联合学习 (FL) 面临着平衡隐私,安全和公平的挑战. 这项调查探讨了它们的相互联系,表明公平可以为强大的FL框架提供隐私和安全的桥梁.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 网络安全 网络安全
背景情况:
- 联合学习 (FL) 实现了分散的培训,但面临着隐私,安全和公平性的挑战.
- 现有的研究往往孤立这些问题,忽视了它们复杂的相互依存关系.
研究的目的:
- 在佛罗里达州全面分析隐私,安全和公平之间的相互作用.
- 确定这些方面之间的关键权衡和相互联系.
- 提出公平性作为FL优化和保护的统一概念.
主要方法:
- 对FL隐私,安全和公平性的现有文献进行系统的审查和分析.
- 基于已识别的威胁和防御机制对研究进行分类.
- 探索梯度共享在隐私和安全风险中的作用.
主要成果:
- 证明了复杂的相互依存关系:提高公平性可能会影响隐私,安全措施可能会影响公平性.
- 确定公平性作为潜在的桥梁,影响隐私和安全策略.
- 突出了梯度共享作为导致FL风险的关键因素.
结论:
- 一个平衡的FL框架需要解决隐私,安全和公平的交织性质.
- 未来的研究应该专注于开发考虑这些相互依存的综合解决方案.
- 建立强大的防御机制和促进公平对于值得信赖的FL至关重要.
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