多头嵌入联邦学习提升电力消耗预测精度，兼顾隐私与协作。
Heterogeneous Federated Learning Systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism
- 采用多头网络独立参与联邦学习，通过2维向量嵌入共享知识。
- 相比基准模型，预测误差降低24.9%至94.1%，显著提升性能。
- 适合需要跨设备协作且保护数据隐私的智能电网场景。
时间序列预测在智能工厂、智能交通等领域日益普及。现有电力消耗预测模型缺乏对多方客户端协同学习与隐私保护的讨论。为此，我们提出多头异构联邦学习（MHHFL）系统，包含多个独立运作的头网络，各自作为联邦学习载体。在联邦阶段，各头网络被嵌入为二维向量并共享至中心源池；MHHFL随后选择合适源网络，并融合头网络实现知识迁移。实验表明，所提MHHFL系统显著优于基准与先进系统，预测误差降低24.9%至94.1%。消融实验验证了头网络嵌入与选择机制的有效性，显著优于传统联邦平均与随机迁移。
原文摘要 · Abstract (English)
Time-series prediction is increasingly popular in a variety of applications, such as smart factories and smart transportation. Researchers have used various techniques to predict power consumption, but existing models lack discussion of collaborative learning and privacy issues among multiple clients. To address these issues, we propose Multi-Head Heterogeneous Federated Learning (MHHFL) systems that consist of multiple head networks, which independently act as carriers for federated learning. In the federated period, each head network is embedded into 2-dimensional vectors and shared with the centralized source pool. MHHFL then selects appropriate source networks and blends the head networks as knowledge transfer in federated learning. The experimental results show that the proposed MHHFL systems significantly outperform the benchmark and state-of-the-art systems and reduce the prediction error by 24.9% to 94.1%. The ablation studies demonstrate the effectiveness of the proposed mechanisms in the MHHFL (head network embedding and selection mechanisms), which significantly outperforms traditional federated average and random transfer.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。