Privacy-Preserving Spatial Crowdsourcing Drone Services for Post-Disaster Infrastructure Monitoring: A Conditional Federated Learning Approach
<p dir="ltr">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 p...
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2025
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| _version_ | 1864513532422258688 |
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| author | Junaid Akram (13389717) |
| author2 | Awais Akram (11890289) Palash Ingle (21512921) Rutvij H. Jhaveri (16850751) Ali Anaissi (2837591) Adnan Akhunzada (20151648) |
| author2_role | author author author author author |
| author_facet | Junaid Akram (13389717) Awais Akram (11890289) Palash Ingle (21512921) Rutvij H. Jhaveri (16850751) Ali Anaissi (2837591) Adnan Akhunzada (20151648) |
| author_role | author |
| dc.creator.none.fl_str_mv | Junaid Akram (13389717) Awais Akram (11890289) Palash Ingle (21512921) Rutvij H. Jhaveri (16850751) Ali Anaissi (2837591) Adnan Akhunzada (20151648) |
| dc.date.none.fl_str_mv | 2025-06-09T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/jstars.2025.3577648 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Privacy-Preserving_Spatial_Crowdsourcing_Drone_Services_for_Post-Disaster_Infrastructure_Monitoring_A_Conditional_Federated_Learning_Approach/29274449 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Civil engineering Communications engineering Control engineering, mechatronics and robotics Information and computing sciences Artificial intelligence Distributed computing and systems software Machine learning Anomaly detection Drones Federated learning (FL) Spatial crowdsourcing Structural health monitoring (SHM) |
| dc.title.none.fl_str_mv | Privacy-Preserving Spatial Crowdsourcing Drone Services for Post-Disaster Infrastructure Monitoring: A Conditional Federated Learning Approach |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/jstars.2025.3577648" target="_blank">https://dx.doi.org/10.1109/jstars.2025.3577648</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_4f276acfc2bd63b60eb047b5f9660470 |
| identifier_str_mv | 10.1109/jstars.2025.3577648 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29274449 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Privacy-Preserving Spatial Crowdsourcing Drone Services for Post-Disaster Infrastructure Monitoring: A Conditional Federated Learning ApproachJunaid Akram (13389717)Awais Akram (11890289)Palash Ingle (21512921)Rutvij H. Jhaveri (16850751)Ali Anaissi (2837591)Adnan Akhunzada (20151648)EngineeringCivil engineeringCommunications engineeringControl engineering, mechatronics and roboticsInformation and computing sciencesArtificial intelligenceDistributed computing and systems softwareMachine learningAnomaly detectionDronesFederated learning (FL)Spatial crowdsourcingStructural health monitoring (SHM)<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/jstars.2025.3577648" target="_blank">https://dx.doi.org/10.1109/jstars.2025.3577648</a></p>2025-06-09T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/jstars.2025.3577648https://figshare.com/articles/journal_contribution/Privacy-Preserving_Spatial_Crowdsourcing_Drone_Services_for_Post-Disaster_Infrastructure_Monitoring_A_Conditional_Federated_Learning_Approach/29274449CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/292744492025-06-09T03:00:00Z |
| spellingShingle | Privacy-Preserving Spatial Crowdsourcing Drone Services for Post-Disaster Infrastructure Monitoring: A Conditional Federated Learning Approach Junaid Akram (13389717) Engineering Civil engineering Communications engineering Control engineering, mechatronics and robotics Information and computing sciences Artificial intelligence Distributed computing and systems software Machine learning Anomaly detection Drones Federated learning (FL) Spatial crowdsourcing Structural health monitoring (SHM) |
| status_str | publishedVersion |
| title | Privacy-Preserving Spatial Crowdsourcing Drone Services for Post-Disaster Infrastructure Monitoring: A Conditional Federated Learning Approach |
| title_full | Privacy-Preserving Spatial Crowdsourcing Drone Services for Post-Disaster Infrastructure Monitoring: A Conditional Federated Learning Approach |
| title_fullStr | Privacy-Preserving Spatial Crowdsourcing Drone Services for Post-Disaster Infrastructure Monitoring: A Conditional Federated Learning Approach |
| title_full_unstemmed | Privacy-Preserving Spatial Crowdsourcing Drone Services for Post-Disaster Infrastructure Monitoring: A Conditional Federated Learning Approach |
| title_short | Privacy-Preserving Spatial Crowdsourcing Drone Services for Post-Disaster Infrastructure Monitoring: A Conditional Federated Learning Approach |
| title_sort | Privacy-Preserving Spatial Crowdsourcing Drone Services for Post-Disaster Infrastructure Monitoring: A Conditional Federated Learning Approach |
| topic | Engineering Civil engineering Communications engineering Control engineering, mechatronics and robotics Information and computing sciences Artificial intelligence Distributed computing and systems software Machine learning Anomaly detection Drones Federated learning (FL) Spatial crowdsourcing Structural health monitoring (SHM) |