联合学习与保证隐私和声誉驱动的激励措施,以互联网的车辆
Jiayong Chai1, Mo Chen2, Wei Zhang3
1School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
FLARE架构通过整合保护隐私的联合学习 (FL) 和区块链激励来增强智能系统的跨域数据协作. 它确保了数据安全,并提高了参与车辆数据应用程序的质量.
科学领域:
- * 智能交通运输系统
- * 数据安全与隐私
- * 机器学习 * 机器学习
背景情况:
- *跨领域的数据协作对于汽车互联网等智能系统至关重要,但数据孤岛和隐私问题阻碍了进展.
- *现有的联合学习 (FL) 和区块链解决方案提供可追溯性,但存在隐私泄露风险和无效的激励措施.
- *缺乏信任和延迟奖励降低了参与协作数据倡议的质量.
研究的目的:
- * 提出以保证隐私和声誉驱动激励 (FLARE) 的联合学习架构.
- * 解决当前跨领域数据协作计划中的隐私泄露和激励机制限制.
- * 增强数据共享环境中的信任,安全和参与质量.
主要方法:
- * 开发FLARE架构,整合加密安全和机制设计.
- * 实施使用部分同态加密和零知识证明的安全和忠实执行的梯度聚合 (SafeGrad) 协议.
- * 链上经济 (EconChain) 激励机制的设计,具有即时的微奖励和动态声誉模型.
主要成果:
- *FLARE为梯度贡献提供可验证的隐私保证,防止反转攻击.
- *EconChain机制确保了培训贡献的精确测量和可持续激励.
- *实验表明,FLARE可以提高节点参与和贡献质量,而不会牺牲模型的准确性.
结论:
- *FLARE为可靠和高效的跨领域数据流通提供了一个新的范式.
- * 架构有效地平衡了强大的安全性与协作学习中的高活力.
- * FLARE 提高了参与者参与度和数据质量,用于像智能运输这样的敏感应用.
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