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 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.