提升多代理式多微电网能源管理的联合学习的通信效率
IEEE transactions on neural networks and learning systems
|October 1, 2024
概括
本研究介绍了一种通信效率高的联合学习 (CEFL) 算法,以减少多微电网 (MMG) 能源管理中的通信负担. 该CEFL算法通过选择性上传层并使用聚合模型进行参数更新来提高代理性能.
科学领域:
- 能源系统 能源系统
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 用户隐私对于在多微电网 (MMG) 中高效的多代理能源管理至关重要.
- 联合学习 (FL) 提供隐私保护,但面临沟通负担和融合不一致.
- 在FL中的层融合速度差异可以降低总体剂的性能.
研究的目的:
- 为MMG能源管理提出一个通信效率高的联合学习 (CEFL) 算法.
- 解决现有的FL方法中的通信负担和层不一致问题.
- 提高MMG系统中代理人的性能和融合率.
主要方法:
- 开发了一种使用Shapley值 (SV) 的层评估 (LE) 机制,只识别和上传高贡献层.
- 实施了一种通信效率高的参数聚合方法,用于全球模型 (GM) 更新的聚合模型 (AM).
- 通过对3-8个微网格的MMG进行数值分析,验证了CEFL算法.
主要成果:
- 与标准FL相比,拟议的CEFL算法大大降低了通信开销.
- CEFL显示了更好的融合率和总体代理业绩.
- 实验证实了对四个最先进的算法进行性能增强.
结论:
- 在MMG的能源管理中,CEFL算法有效地平衡了隐私保护和通信效率.
- 在这种情况下,层评估和聚合模型机制是提高FL性能的关键.
- CEFL为可扩展和高效的去中心化能源管理系统提供了一个有前途的解决方案.
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