Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/30988
Title: Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance
Authors: Dong, L
Peng, Y
Jiang, F
Wang, K
Yang, K
Keywords: explainable AI;federated learning;fire surveillance;semantic communication (SC)
Issue Date: 12-Sep-2024
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: Dong, L. et al. (2024) 'Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance', IEEE Transactions on Industrial Informatics, 20 (12), pp. 14053 - 14061. doi: 10.1109/TII.2024.3441626.
Abstract: 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 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 strategy to provide a specific SC model for each device according to its Fisher information matrix, thus overcoming the heterogeneity. Next, an Explainable SC 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.
URI: https://bura.brunel.ac.uk/handle/2438/30988
DOI: https://doi.org/10.1109/TII.2024.3441626
ISSN: 1551-3203
Other Identifiers: ORCiD: Li Dong https://orcid.org/0000-0002-0127-8480
ORCiD: Yubo Peng https://orcid.org/0000-0001-9684-2971
ORCiD: Feibo Jiang https://orcid.org/0000-0002-0235-0253
ORCiD: Kezhi Wang https://orcid.org/0000-0001-8602-0800
ORCiD: Kun Yang https://orcid.org/0000-0002-6782-6689
Appears in Collections:Dept of Computer Science Research Papers

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