International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Time Series Crime Prediction Using a Federated Machine Learning Model

  • Salam, Mustafa Abdul (Artificial Intelligence Dept., Faculty of Computers and Artificial Intelligence, Benha University) ;
  • Taha, Sanaa (Information Technology Dept., Faculty of Computers and Artificial Intelligence, Cairo University) ;
  • Ramadan, Mohamed (Computer Science Dept., Faculty of Computers and Information, Egyptian E-Learning University)
  • Received : 2022.04.05
  • Published : 2022.04.30
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Abstract

Crime is a common social problem that affects the quality of life. As the number of crimes increases, it is necessary to build a model to predict the number of crimes that may occur in a given period, identify the characteristics of a person who may commit a particular crime, and identify places where a particular crime may occur. Data privacy is the main challenge that organizations face when building this type of predictive models. Federated learning (FL) is a promising approach that overcomes data security and privacy challenges, as it enables organizations to build a machine learning model based on distributed datasets without sharing raw data or violating data privacy. In this paper, a federated long short- term memory (LSTM) model is proposed and compared with a traditional LSTM model. Proposed model is developed using TensorFlow Federated (TFF) and the Keras API to predict the number of crimes. The proposed model is applied on the Boston crime dataset. The proposed model's parameters are fine tuned to obtain minimum loss and maximum accuracy. The proposed federated LSTM model is compared with the traditional LSTM model and found that the federated LSTM model achieved lower loss, better accuracy, and higher training time than the traditional LSTM model.

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References

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