基于区块链的非IID数据的安全和分散的联合学习框架
Feng Zhang1, Yongjing Zhang1, Shan Ji1
1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.
Heliyon
|April 2, 2024
概括
本研究引入了使用区块链的层次联合学习框架,以应对与非独立和相同分布 (非IID) 数据的挑战. 这种新的方法提高了模型的准确性,并确保了分散式机器学习中的隐私.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 区块链技术 区块链技术
背景情况:
- 联合学习在没有数据共享的情况下促进了协作模式培训,但在与非独立和相同分布的 (非IID) 数据方面存在困难.
- 跨组织的非IID数据分布显著挑战了传统的联合学习性能和模型准确性.
研究的目的:
- 提出一个层次化的联合学习框架,利用区块链技术来增强非IID数据培训.
- 为了改善数据隐私,安全性和分散环境中的整体联合学习性能.
主要方法:
- 开发了一个区块链系统,以创建一个全球共享池,减少本地数据非IID程度,提高模型准确性.
- 利用智能合约来实现分散的模型分布,收集和聚合在主区块链上.
- 在MNIST,时尚-MNIST和CIFAR-10数据集上训练了多层感知器 (MLP) 和卷积神经网络 (CNN) 模型.
主要成果:
- 拟议的框架显著提高了去中心化联合学习模型的准确性.
- 在处理多个基准数据集的非IID数据方面表现出有效性.
- 验证了基于区块链的分层联合学习方法的可行性和性能增强.
结论:
- 层次化的联合学习框架有效地解决了分散的环境中的非IID数据挑战.
- 区块链集成增强了联邦学习中的隐私,安全和性能.
- 拟议的方法为具有异质数据分布的协作机器学习提供了一个强大的解决方案.
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