可概括的异质联合交叉相关性和实例相似性学习
IEEE transactions on pattern analysis and machine intelligence
|October 25, 2023
概括
联合相关性和相似性学习 (FCCL+) 通过使用公共数据和非目标蒸来解决模型异质性和灾难性遗忘,从而增强联合学习. 这既提高了域内可区分性,也提高了域内通用性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机科学 计算机科学
背景情况:
- 联合学习可以在分散的私人数据上进行协作模式培训.
- 模型异质性和灾难性遗忘是联合学习的关键局限性.
研究的目的:
- 引入FCCL+,一种新的联合学习方法,以应对异质性和灾难性遗忘.
- 在联合学习中增强域内可区分性和域内通用性.
主要方法:
- 利用不相关的未标记的公共数据来弥合异质参与者之间的沟通差距.
- 构建交叉相关性矩阵,并在logit和特征级别对准实例相似性分布.
- 实施联合非目标蒸,以保留跨领域的知识并防止优化冲突.
主要成果:
- FCCL+有效地克服了模型异质性造成的通信障碍.
- 该方法通过保留跨领域的知识来提高通用能力.
- 经验结果证明了FCCL+在各种场景中的优越性和模块效率.
结论:
- 对于异质的联合学习挑战,FCCL+提供了一个强大的解决方案.
- 拟议的基准标准有助于对联合学习方法进行标准化评估.
- 这种方法显著提高了联合学习的适用性和通用性.
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