通过探索标签相关性,通过仅使用积极标签的联合学习.
概括
针对多标签分类 (MLC) 的联合学习在隐私和性能方面存在困难. 我们的FedALC方法探索标签相关性,以显著改善模型培训和安全.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 联合学习 (FL) 能够在分散的数据源中进行协作模式培训,同时保持用户隐私.
- 在FL环境中,多标签分类 (MLC) 面临着挑战,特别是每个客户不平衡的仅为积极的数据,导致表现不佳.
- 使用服务器端调节器或频繁的私人嵌入交换的现有方法在解决标签相关性和通信成本方面存在局限性.
研究的目的:
- 提出一种新的联合学习方法,有效地应对多标签分类的挑战.
- 在FL框架内利用标签相关性来提高模型性能.
- 为了提高安全性,并减少联合MLC的通讯开销.
主要方法:
- 用标签相关性估计和利用标签相关性在课堂嵌入学习中的联邦平均化 (FedALC).
- 一种FedALC的变体建议学习每个客户端的固定类嵌入,以最大限度地减少服务器-客户端通信.
- 该方法侧重于通过利用不同标签对之间的关系来改善模型培训.
主要成果:
- 在多个数据集中,FedALC显著优于现有的联合MLC方法.
- 拟议的变体增强了数据安全性,并通过只交换一次类嵌入来减少通信开销.
- 这种方法有效地利用标签相关性来提高分类准确性.
结论:
- 在联合学习环境中,FedALC为多标签分类提供了强大而高效的解决方案.
- 该方法通过结合标签相关性和优化沟通,成功解决了以前方法的局限性.
- 这项工作为保护隐私的协作机器学习提供了宝贵的进步.
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