LstFcFedLear: A LSTM-FC with Vertical Federated Learning Network for Fault Prediction

• Main Authors: Xiangquan Zhang, Zhili Ma, Anmin Wang, Haifeng Mi, Junjun Hang
• Format: Article
• Language: English
• Published: Hindawi-Wiley 2021-01-01
• Series: Wireless Communications and Mobile Computing
• Online Access: http://dx.doi.org/10.1155/2021/2668761

The firefighting IoT platform links multiple firefighting subsystems. The data of each subsystem belongs to the sensitive data of the profession. Failure prediction is a crucial topic for firefighting IoT platforms, because failures may cause equipment injuries. Currently, in the maintenance of fire IoT terminal equipment, fault prediction based on equipment time series has not been included. The use of intelligent technology to continuously predict the failure of firefighting IoT equipment can not only eliminate the intervention of regular maintenance but also provide early warning of upcoming failures. In order to solve this problem, we propose a vertical federated learning framework based on LSTM fault classification network (LstFcFedLear). The advantage of this framework is that it can encrypt and integrate the data on the entire firefighting IoT platform to form a new dataset. After the synthesized data is trained through each model, the optimal model parameters can be finally updated. At the same time, it can ensure that the data of each business system is not leaked. The framework can predict when IoT equipment will fail in the future and then provide what measures should be used. The experimental results show that the LstFcFedLear model provides an effective method for fault prediction, and its results are comparable to the baseline.