相关实验视频
MEFL:对物联网中的不平衡数据进行元平衡联合学习
Jialu Tang1, Yali Gao1, Xiaoyong Li1
1The Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Entropy (Basel, Switzerland)
|June 26, 2025
概括
超平衡联合学习 (MEFL) 解决了物联网 (IoT) 中的数据不平衡问题. 这种新的方法提高了联邦学习 (FL) 模型的准确性和稳定性,改善了个性化的物联网应用程序的概括性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 物联网的物联网,就是物联网.
背景情况:
- 物联网环境中的数据异质性给联合学习 (FL) 模型带来了重大挑战.
- 不平衡和非IID数据分布降低了模型的准确性,稳定性和概括能力.
研究的目的:
- 提出一种新的方法,即Meta-Equilibrized Federated Learning (MEFL),以解决数据异质性,并提高物联网中的FL性能.
- 提高全球和当地优化目标在FL的一致性.
主要方法:
- MEFL将元学习与梯度下降保护相结合.
- 它采用平衡优化聚合机制,使用梯度相似性和方差加权调整.
- 该方法减轻了多步本地更新的梯度偏差.
主要成果:
- 与基线方法相比,MEFL在最终测试准确度中至少提高了3.26%.
- 这种方法大大减少了通讯开支.
- 在真实世界数据集上展示了卓越的性能和概括能力.
结论:
- 在FL,MEFL有效地解决了全球和本地优化目标之间的不一致性.
- 该方法优化了针对个性化物联网应用的本地和全球模型之间的权衡.
- MEFL为物联网中的跨域数据安全部署提供了一个高效的解决方案.
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