Privacy-Preserving Spatial Crowdsourcing Drone Services for Postdisaster Infrastructure Monitoring: A Conditional Federated Learning Approach
Sixth-generation (6G) networks, offering ultra-low latency and high bandwidth, provide critical support for rapid data transmission in postdisaster environments where conventional infrastructure may be compromised. This study presents a privacy-preserving framework for postdisaster structural health...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11027713/ |
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| Summary: | Sixth-generation (6G) networks, offering ultra-low latency and high bandwidth, provide critical support for rapid data transmission in postdisaster environments where conventional infrastructure may be compromised. This study presents a privacy-preserving framework for postdisaster structural health monitoring (SHM) by integrating 6G-enabled Internet of Drone Things and spatial crowdsourcing. Drones and unmanned ground vehicles collect real-time imagery of damaged infrastructure. To address privacy concerns and reduce communication overhead, we employ federated learning (FL), which enables decentralized model training on local devices without transmitting raw data. A key challenge in FL is the presence of nonindependent and identically distributed data across clients, which degrades global model performance. To mitigate this, we propose personalized conditional federated averaging (PC-FedAvg), a selective aggregation method that incorporates only client models with low validation loss into the global update. The PC-FedAvg framework is built on EfficientNetV2 and includes personalized model adaptation to enhance generalization on heterogeneous data. Experimental results on the “Concrete Crack Images for Classification” dataset demonstrate that PC-FedAvg outperforms baseline FL methods in accuracy and stability. This approach improves the effectiveness and reliability of SHM systems in real-world postdisaster scenarios by enabling timely and accurate damage assessment while preserving data privacy. |
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| ISSN: | 1939-1404 2151-1535 |