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Generalized Anxiety Disorder (GAD) is a chronic condition characterized by excessive and uncontrollable worry that persists for at least six months, significantly interfering with daily functioning. Unlike situational anxiety, which arises in response to specific stressors, GAD often occurs without a clear cause. Individuals may experience disproportionate worry about work, health, or relationships. For instance, a person might continuously fear poor health despite normal medical evaluations or...
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The generalized Hooke's Law is a broadened version of Hooke's Law, which extends to all types of stress and in every direction. Consider an isotropic material shaped into a cube subjected to multiaxial loading. In this scenario, normal stresses are exerted along the three coordinate axes. As a result of these stresses, the cubic shape deforms into a rectangular parallelepiped. Despite this deformation, the new shape maintains equal sides, and there is a normal strain in the direction of the...
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    联邦学习 (FL) 的语义细分与新数据作斗争,导致灾难性的遗忘. 我们的层次性遗忘缓解 (HFA) 模型有效地解决了客户内部和客户之间的遗忘,使新类别的持续学习成为可能.

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    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 联合学习 (FL) 通过分散的培训,通过降低注释成本来增强语义细分.
    • 现有的语义细分FL方法在客户端在流数据中遇到新类时遭受灾难性遗忘.
    • 客户之间异质的忘记被新客户与新课程的不规则参与加剧了.

    研究的目的:

    • 提出一种新的等级遗忘缓解 (HFA) 模型,以解决基于FL的语义细分中的灾难性遗忘.
    • 确保在所有当地客户中不断学习新类别,同时保持对旧类别的了解.
    • 在动态FL环境中减轻客户内部和客户间的遗忘.

    主要方法:

    • 开发了一种信任规范化伪标签策略,用于类平衡的软伪标签,以减轻客户内部的遗忘.
    • 设计了一个图形诱导的关系匹配损失和一个忘记平衡梯度传播模块来处理旧的类关系.
    • 实现了任务检测模块和自适应DBSCAN集群,以解决客户间异质遗忘和管理全球模型更新.

    主要成果:

    • 拟议的HFA模型有效地缓解了当地客户的阶级不平衡遗忘.
    • 该模型成功地解决了模两可的阶级间关系和阶级不平衡的梯度传播.
    • 实验表明HFA在多个数据集上优于现有方法.

    结论:

    • 在联邦语义细分中,HFA模型为灾难性遗忘提供了一个强大的解决方案.
    • 提出的方法可以在动态,异质的FL环境中有效地保留和转移知识.
    • HFA确保所有客户可以相互学习,同时不断适应新的数据和类.