Transmission line foreign object detection algorithm based on federated split learning
Foreign object intrusion is one of the primary causes of power transmission line failures.However,existing research on power transmission line foreign object detection has not fully utilized the computational capabilities of terminal devices,leading to issues such as resource waste and privacy breaches.In response to these problems,in this paper we proposed a federated split learning detection algorithm(FSLDA).This model integrates federated learning and split learning to enhance the efficiency and data security of foreign object detection systems.The FSLDA,by developing a divisible small-scale neural network,distributes the computational workload across different devices,thereby reducing the computing pressure on devices and ensuring the privacy security of training data is effectively guaranteed.Experimental results demonstrate that,compared to classic federated learning,FSLDA reduces the training time and the energy consumption by 10%and 20%,respectively,while maintaining the prediction accuracy.Thus,FSLDA is effective in enhancing the efficiency and reliability of power transmission line foreign object detection,contributing to the optimization of overall system performance and the safeguarding of data privacy.
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