用可解释语义联邦学习提升工业边缘火灾监控的隐私与效率
Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance
- 通过可解释语义联邦学习保护数据隐私，实现安全模型训练
- 基于费舍尔信息矩阵自适应调整设备端模型，解决异构设备问题
- 引入leakyReLU激活映射机制，可视化语义与原始数据的关系
在火灾监测中，工业物联网（IIoT）设备需频繁传输大量监控数据，导致频谱资源消耗巨大。为此，我们提出工业边缘语义网络（IESN），使IIoT设备通过语义通信（SC）发送预警信息。需解决三大挑战：（1）数据隐私与安全；（2）异构设备下的语义模型适配；（3）语义可解释性。为此，首先提出可解释语义联邦学习（XSFL），在保障数据隐私与安全的前提下训练语义模型。其次，设计自适应客户端训练（ACT）策略，根据各设备的费舍尔信息矩阵生成定制化语义模型，以应对设备异构性。再者，提出可解释语义通信（ESC）机制，采用leakyReLU基激活映射，揭示提取语义与原始监测数据间的对应关系。最后，仿真结果验证了XSFL的有效性。
原文摘要 · Abstract (English)
In fire surveillance, Industrial Internet of Things (IIoT) devices require transmitting large monitoring data frequently, which leads to huge consumption of spectrum resources. Hence, we propose an Industrial Edge Semantic Network (IESN) to allow IIoT devices to send warnings through Semantic communication (SC). Thus, we should consider (1) Data privacy and security. (2) SC model adaptation for heterogeneous devices. (3) Explainability of semantics. Therefore, first, we present an eXplainable Semantic Federated Learning (XSFL) to train the SC model, thus ensuring data privacy and security. Then, we present an Adaptive Client Training (ACT) strategy to provide a specific SC model for each device according to its Fisher information matrix, thus overcoming the heterogeneity. Next, an Explainable SC (ESC) mechanism is designed, which introduces a leakyReLU-based activation mapping to explain the relationship between the extracted semantics and monitoring data. Finally, simulation results demonstrate the effectiveness of XSFL.
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