基于区块链的联合学习系统:关于设计选择的调查
Yustus Eko Oktian1, Sang-Gon Lee1
1College of Software Convergence, Dongseo University, Busan 47011, Republic of Korea.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
在不可信任的环境中联合学习从区块链集成中受益. 这项调查分析了基于区块链的联合学习设计,揭示了公平性,稳定性和效率之间的权衡,突出了未来研究的领域.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 网络安全 网络安全
背景情况:
- 联合学习 (FL) 传统上假定可信环境,限制其在现实世界的场景中应用,需要不值得信赖的各方之间的合作.
- 区块链技术提供了一个去中心化和不变的平台,使其成为增强对联合学习系统信任的有希望的解决方案.
- 最近的研究兴趣集中在将区块链与联合学习集成,以解决安全和信任问题.
研究的目的:
- 对基于区块链的联合学习 (BFL) 系统进行全面的文献调查.
- 分析用于克服BFL的挑战的常见设计模式和变化.
- 根据关键性能指标评估不同设计选择的优缺点.
主要方法:
- 对现有的BFL研究进行系统的文献审查.
- 在BFL系统中识别和分类了大约31种不同的设计变化.
- 对设计模式的分析,考虑强度,效率,隐私和公平性.
主要成果:
- 在公平性和稳健性之间观察到线性关系,表明公平性的改进可以提高稳健性.
- 在同时改善所有指标和整体系统效率之间存在一种权衡.
- 确定了BFL中受欢迎的设计选择和需要进一步开发的领域.
结论:
- 未来的BFL系统需要在模型压缩和异步聚合技术方面取得进展.
- 在BFL研究中,需要更严格的系统效率评估.
- 进一步的研究应侧重于将BFL应用于跨设备设置,并解决已识别的效率权衡问题.
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