雷姆:三阶段的斯泰克尔伯格游戏,用于层次化的联合学习,具有声誉意识的激励机制
Cuihua Zuo1, Peihua Xu2,3, Yachen Song1
1School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, 430024, China.
Scientific reports
|October 2, 2025
概括
这项研究介绍了对等级联合学习 (HFL) 的声誉意识激励机制 (RAIM). 雷姆通过识别不可靠的设备和优化参与,提高设备合作和系统性能.
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:
- 层次联合学习 (HFL) 提高了效率和个性化,但面临着不可靠的设备和战略参与者的挑战.
- 在HFL中现有的激励机制对恶意行为或设备故障无效,损害了模型质量.
- 设备异质性和参与者的战略行为进一步降低了当前HFL激励系统的有效性.
研究的目的:
- 提出一个知名度意识激励机制 (RAIM),以优化HFL的节点合作和系统性能.
- 解决HFL环境中不可靠的设备和战略行为所带来的挑战.
- 提高通过HFL培训的全球模型的整体质量和可靠性.
主要方法:
- 根据培训质量和历史数据对终端设备的声誉评估,以检测恶意攻击.
- 利用联盟区块链来维护参与者的声誉,确保透明度和公平性.
- 模拟HFL交互作为一个三阶段的Stackelberg游戏,以获得独特的平衡,并设计一个最佳的服务器选择算法.
主要成果:
- 拟议的RAIM有效地识别和减轻HFL中不可靠和恶意设备的影响.
- 斯塔克尔伯格游戏模型成功地解决了层次决策,并确定了最佳的平衡.
- 最佳的服务器选择算法鼓励高声誉,低成本的设备,最大限度地提高系统性能和社会实用性.
结论:
- 在分层联合学习中,RAIM显著提高了节点合作和系统性能.
- 该机制提供了一个强大的解决方案,用于处理不可靠的设备和HFL的战略行为.
- 在广泛的实验评估中,RAIM与现有方法相比表现优越.
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