Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4316
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dc.contributor.authorSwain, Sipra-
dc.contributor.authorKhilar, Pabitra Mohan-
dc.contributor.authorSwain, Rakesh Ranjan-
dc.contributor.authorSenapati, Biswa Ranjan-
dc.date.accessioned2024-01-17T11:27:49Z-
dc.date.available2024-01-17T11:27:49Z-
dc.date.issued2023-12-
dc.identifier.citation20th IEEE India Council Conference (INDICON), CMRIT Hyderabad, India, 14-17 December 2023en_US
dc.identifier.urihttp://hdl.handle.net/2080/4316-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractThis paper proposes a federated learning-based framework for fault detection in an unmanned aerial vehicle (UAV)-reliable sensor network. The proposed approach is implemented in three stages. In the first stage of implementation, an experimental setup is done for real-time sensor data collection using a UAV module. In the second stage of implementation, a three-tier architecture is designed to perform a federated learning computation. In the third stage, a probabilistic neural network model is implemented in the federated learning framework for fault detection and classification. The real-time data set is used for the validation of the model in terms of fault detection rate, false alarm rate, and false positive rate. The distributed framework ensured data security and optimal resource utilization, which increased the quality of service (QoS) performance of the UAVreliable sensor network.en_US
dc.subjectFederated Learningen_US
dc.subjectFault Diagnosisen_US
dc.subjectSensoren_US
dc.subjectUAVen_US
dc.subjectPNNen_US
dc.titleA Federated Learning Based Fault Diagnosis in UAV-Reliable Sensor Networken_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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