云端端协作联合学习:在非IID环境中提高模型准确性和隐私
Ling Li1, Lidong Zhu1, Weibang Li2
1National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu 611731, China.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
本研究为云端端系统引入了一种保护隐私的联合学习方法. 它有效地处理非独立和相同分布的 (非IID) 数据,提高模型准确性和保护终端节点数据隐私.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 分布式系统 分布式系统
背景情况:
- 云端端计算架构面临着来自不同终端节点的非独立和相同分布 (非IID) 数据的挑战.
- 平衡数据异质性和隐私保护对于有效的大规模边缘数据处理至关重要.
研究的目的:
- 为云端边缘端协作量身定制的新型保护隐私的联合学习方法提出建议.
- 解决边缘计算环境中非IID数据所带来的挑战.
- 在分布式系统中增强模型准确性和数据隐私.
主要方法:
- 一种保护隐私的联合学习方法,利用云端边缘端协作.
- 根据数据分布相似性对终端节点进行分组,并构建协作边缘子网络.
- 使用合成数据生成的注意力机制,增强带有梯度惩罚的瓦斯斯坦生成对抗网络 (WGAN-GP).
- 实施数据重新抽样和损失函数权重,以减轻不平衡数据带来的偏差.
主要成果:
- 拟议的方法有效地减轻了非IID数据对全球模型准确性的负面影响.
- 增强的WGAN-GP成功生成平衡的合成数据,同时保持原始数据模式和隐私.
- 数据重新抽样和损失加权的策略可以减少模型偏差.
- 实验结果显示,与现有方法相比,模型准确度和F1得分显著改善.
结论:
- 开发的云端端联合学习方法为处理非IID数据提供了强大的解决方案,同时确保隐私.
- 该方法在异质边缘环境中增强了分布式机器学习的性能.
- 这项工作有助于通过协作学习更准确,更安全的边缘数据分析.
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