一个可扩展的区块链支持的联邦学习架构,用于边缘计算
Shuyang Ren1, Eunsam Kim2, Choonhwa Lee3
1School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, China.
PloS one
|August 16, 2024
概括
本研究介绍了FLCoin,这是一个新的区块链和边缘计算的联合学习 (FL) 系统. 通过优化共识处理,FLCoin提高了物联网 (IoT) 网络的效率和可扩展性.
科学领域:
- * 分布式系统和人工智能
- * 区块链技术和边缘计算
背景情况:
- * 现有的深度学习和区块链解决方案用于边缘数据处理,往往忽视了物联网 (IoT) 环境中的区块链共识机制的重大资源需求.
- *联合学习 (FL) 为分布式机器学习提供了一种保护隐私的方法,但需要有效地与底层网络基础设施集成.
研究的目的:
- * 提出和评估FLCoin,这是一个集成区块链和联合学习的新系统,用于物联网网络中高效的边缘数据处理.
- *通过优化共识处理和减少资源开销来解决目前基于区块链的联合学习方法的局限性.
主要方法:
- * 开发一个针对联合学习 (FL) 处理的双层区块链架构.
- * 引入一种新的以委员会为基础的共识机制,委员会成员通过FL过程选举.
- *使用MNIST数据集进行实验验证,以训练卷积神经网络 (CNN) 模型.
主要成果:
- *不论网络大小,FLCoin都显示出稳定的通信开销,确保了系统的可扩展性.
- * 即使增加了参与节点,共识延迟仍低于3秒,导致整体训练时间减少.
- * 与使用PBFT共识的类似系统相比,实现了90%的通信开支降低,培训时间成本降低35%.
结论:
- * FLCoin提供了一种高效且可扩展的解决方案,用于在物联网边缘网络中集成区块链和联合学习.
- * 拟议的架构有效地减少了共识处理的资源需求,使其适用于资源受限的物联网环境.
- * FLCoin通过安全高效的分布式智能为开发先进智能物联网服务奠定了坚实的基础.
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