Summary of Explainable Semantic Federated Learning Enabled Industrial Edge Network For Fire Surveillance, by Li Dong et al.
Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance
by Li Dong, Yubo Peng, Feibo Jiang, Kezhi Wang, Kun Yang
First submitted to arxiv on: 28 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Information Theory (cs.IT)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Industrial Edge Semantic Network (IESN) aims to reduce spectrum resource consumption in industrial Internet of Things (IIoT) devices by enabling them to transmit warnings through semantic communication (SC). To achieve this, a three-pronged approach is developed: eXplainable Semantic Federated Learning (XSFL) for data privacy and security, Adaptive Client Training (ACT) for heterogeneous device adaptation, and Explainable SC (ESC) for semantics explanation. The XSFL trains the SC model while ensuring data protection, ACT provides personalized models based on devices’ Fisher information matrices, and ESC maps extracted semantics to monitoring data using a leakyReLU-based activation mapping. Simulation results demonstrate the effectiveness of this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In fire surveillance, IIoT devices need to send warning messages frequently, which uses up a lot of spectrum resources. To fix this, researchers developed a new network called Industrial Edge Semantic Network (IESN). This helps devices share warnings using semantic communication (SC), while keeping data private and secure. They came up with three solutions: one for training the SC model, another for adapting it to different devices, and finally, a way to explain what’s happening when the device is sending or receiving information. |
Keywords
» Artificial intelligence » Federated learning » Semantics
