对于模型-异质联合学习的Allosteric特征协作
IEEE transactions on neural networks and learning systems
|December 25, 2023
概括
本研究介绍了用于模型异质联合学习 (M-hete FL) 的Allosteric特征协作 (AlFeCo). AlFeCo能够在结构不同的客户端模型之间进行知识交换,从而提高协作学习的性能.
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 (FL) 通常假设同质的客户端模型,限制其应用.
- 在FL (M-hete FL) 中的模型异质性存在挑战,原因是无法直接汇总各种客户端模型参数.
研究的目的:
- 提出一种新的方法,即Allosteric Feature Collaboration (AlFeCo),用于在模型异质的联合学习环境中有效的协作学习.
- 通过在结构上不同的客户端模型之间实现知识交换,解决M-hete FL中的参数聚合的挑战.
主要方法:
- 开发了一个全特征生成器,从多个客户端模型中提取与任务相关的信息.
- 利用客户端共享和客户端特定的代码进行知识交换和生成尺寸可变的全特征.
- 实施了双路通信机制 (模型-模型和模型-预测),以监督使用全特征的协作模型更新.
主要成果:
- AlFeCo有效地促进了异质客户模式之间的知识交流.
- 拟议的方法在经典的FL基准指标上表现强.
- 在模型异质的联合反伪造任务中,AlFeCo被证明是有效的.
结论:
- AlFeCo提供了一个可行的解决方案,用于在模型异构的联合学习环境中进行协作学习.
- 该方法增强了不同客户模式之间的沟通和知识共享.
- 理论证据和趋同分析支持AlFeCo在M-hete FL中的有效性.
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