基金会模式的激励机制使得跨部门的联合学习成为可能
Ning Zhang1, Xiaoqing Xu1, Xiaojun Liu1
1Research Institute of China Telecom, Guangzhou, 510630, China.
Scientific reports
|July 7, 2025
概括
本研究引入了联邦学习 (FL) 中基础模型 (FMs) 的新激励框架,以应对知识积和自由骑行等客户问题. 拟议的系统提高了模型的准确性,并过出恶意参与者.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 分布式系统 分布式系统
背景情况:
- 联合学习 (FL) 中的基础模型 (FMs) 提供了高级功能,但引入了战略客户端行为,如知识积和自由骑行.
- 这些行为降低了FM支持的FL环境中的全球模型性能和系统可持续性.
- 现有的激励机制不足以应对这些具体挑战.
研究的目的:
- 为支持FM的FL提出一个新的激励框架,以使客户端和服务器的利益保持一致.
- 为了抑制敌对的客户行为,包括知识积和自由骑行.
- 为了提高全球模型性能和系统可持续性.
主要方法:
- 一个动态的参与者选机制,包括预先选和信任监测,以过低质量的更新,并惩罚不一致的参与.
- 一个成本效益平衡的贡献指标,以基于准确度增长,成本和参与模式来量化客户影响.
- 一个两阶段的Stackelberg游戏,用于模拟适应式服务器定价和客户参与策略,用于共生激励.
主要成果:
- 与现有基准相比,拟议的框架实现了高达21.9%的模型精度.
- 有效地过出恶意客户端,改善整体系统完整性.
- 证明了动态选和贡献指标在协调客户端-服务器交互方面的有效性.
结论:
- 新的激励框架成功地解决了FM-enabled FL.中的战略客户行为.
- 该方法通过激励有益的贡献和惩罚有害的行为来提高模型性能和系统可持续性.
- 这项工作为管理分散的人工智能系统中复杂动态提供了强大的解决方案.
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