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在头尾不平衡数据上的梯度精细化联合学习.

Yiwen Wang, Heye Zhang, Chenchu Xu

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    概括

    联合学习与不平衡的数据作斗争,其中FedGRE (联合梯度改进) 通过改进梯度来增强全球模型. 这种方法可以提高联合长尾分类中的性能,同时保持数据隐私.

    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 分布式系统 分布式系统

    背景情况:

    • 联合学习使分散客户端的协作模式培训能够实现,同时保持数据隐私.
    • 一个关键的挑战是联合的长尾学习,由不平衡的数据分布 (头尾不平衡) 造成,这阻碍了模型性能.
    • 现有的方法难以平衡阶级知识获取与隐私保护,导致低于最佳的全球模型.

    研究的目的:

    • 引入FedGRE (联合梯度精制),这是一个新的方法来应对联合长尾学习的挑战.
    • 在联合学习中开发一种方法,同时解决数据不平衡,并保持严格的数据隐私.
    • 加强在联邦设置中提取类级知识,而不会损害隐私.

    主要方法:

    • FedGRE使用两个机制构建全球梯度:积累扩散和积累精细化.
    • 积累扩散将积累的梯度与随机扰动合并,以减轻阶级失衡.
    • 积累精细化使用积累的梯度作为校准的全球更新的,确保一致性和减少振荡. 一种一致性整合技术将精细的梯度纳入全球模型.

    主要成果:

    • 在6个基准数据集上,FedGRE显著优于14种最先进的方法,用于联合长尾分类.
    • 拟议的方法证明了强大的隐私保护以及更好的分类准确性.

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  • 实验证实了FedGRE在实现阶级平衡的全球优化方面的有效性.
  • 结论:

    • 在联邦式学习中,FedGRE有效地解决了类知识获取和隐私保护之间的冲突.
    • 在FedGRE中,渐变精细化策略在联合长尾学习场景中带来了卓越的性能.
    • 在不平衡的数据集上,FedGRE为构建准确且保护隐私的联合模型提供了一个有前途的解决方案.