删除冗余和基于知识对齐的个性化联合学习,用于在线状态监控
IEEE transactions on neural networks and learning systems
|February 25, 2026
概括
本研究引入了一种新的联合学习 (FL) 框架,用于安全在线监测高压设备中的部分放电 (PD). 它通过优先考虑各种客户端模型并使用空间逻辑对齐来增强全球模型准确性和本地个性化.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 对于部分放电 (PD) 的高压电气设备的在线监控对于安全性和可靠性至关重要.
- 现有系统在数据传输和存储期间面临数据安全和隐私挑战.
- 联合学习 (FL) 提供了一种保护隐私的方法,用于在不共享原始数据的情况下进行协作模式培训.
研究的目的:
- 开发一个先进的FL框架,以便在开关设备中进行强大而安全的在线PD监控.
- 提高全球模型在FL系统中的信息性和代表性.
- 增强客户端模型对本地数据的个性化,同时保持数据隐私.
主要方法:
- 一个新的FL框架,在客户模型评估中采用最大多样性和最小冗余性的策略.
- 引入一个空间逻辑对齐模块与知识蒸用于增强客户端模型个性化.
- 实现混合架构,包括叶子客户端,分支客户端和中央服务器,利用边缘计算.
主要成果:
- 与传统的基于绩效的聚合相比,拟议的框架产生了一个更具信息性和代表性的全球模型.
- 空间逻辑对齐和知识蒸显著改善客户端模型个性化.
- 在多个数据集上的实验验证证明了比最先进的方法更高的性能.
结论:
- 新的FL框架有效地解决了PD监控中的数据安全和隐私问题.
- 基于多样性的模型评估和空间逻辑对齐提高了全球模型的准确性和本地个性化.
- 混合架构和边缘计算集成使高压设备安全的高效,低延迟监控成为可能.
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