SC-NBTI:一个基于智能合约的激励机制,用于联合知识共享
Yuanyuan Zhang1, Jingwen Liu1, Jingpeng Li1
1College of Computer Science, Sichuan University, Chengdu 610065, China.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
这项研究引入了一个新的激励框架,用于联合学习,增强知识共享. 拟议的系统通过解决通信成本和隐私风险,提高了准确性并减少了培训轮次.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 信息系统信息系统信息系统
背景情况:
- 数字知识平台产生了大量的非结构化数据.
- 知识共享受到隐私,通信开销和数据所有权的限制.
- 联合学习可以在没有原始数据交换的情况下进行协作培训,但需要激励参与.
研究的目的:
- 开发一个激励框架,以促进知识协作中的联合学习.
- 解决现有的非合作性游戏激励计划的局限性.
- 通过管理计算和通信成本来激励数据持有者.
主要方法:
- 推出了SC-NBTI:一个智能合约和基于纳什谈判的激励框架.
- 制定奖励分配作为一个合作游戏.
- 采用一种启发式算法来进行纳什讨价还价的解决方案近似.
- 集成的概率梯度散散化用于通信效率和隐私.
主要成果:
- 与DRL-Incentive基线相比,SC-NBTI获得了更高的准确性 (5.89%).
- 该框架需要更少的培训轮进行融合.
- 在FMNIST图像分类任务中表现出有效性.
结论:
- SC-NBTI有效地激励参与知识合作的联合学习.
- 该框架平衡了通信成本,隐私和奖励分配.
- 为数字环境中安全和高效的知识共享提供了一个有前途的解决方案.
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