Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/4316
Title: | A Federated Learning Based Fault Diagnosis in UAV-Reliable Sensor Network |
Authors: | Swain, Sipra Khilar, Pabitra Mohan Swain, Rakesh Ranjan Senapati, Biswa Ranjan |
Keywords: | Federated Learning Fault Diagnosis Sensor UAV PNN |
Issue Date: | Dec-2023 |
Citation: | 20th IEEE India Council Conference (INDICON), CMRIT Hyderabad, India, 14-17 December 2023 |
Abstract: | This 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. |
Description: | Copyright belongs to proceeding publisher |
URI: | http://hdl.handle.net/2080/4316 |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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2023_INDICON_SSwain_AFederated.pdf | 1.58 MB | Adobe PDF | View/Open Request a copy |
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