Research on federal learning techniques for target detection in industrial field images

In the age of data and information, the amount of data is increasing and the privacy protection of data is becoming more and more important. Federated learning model is a distributed deep learning model that can aggregate model parameters from multiple clients while protecting private data. For the scenario of an industrial site where each factory cannot share its private data, the federated learning model allows each local factory to train its own model based on its own data, and then upload the model to a central server for continuous iteration and training. However, because the quantity and quality of data varies from one client to another, this can have an impact on the accuracy of the model. In order to reduce the above impact, an optimisation method that adds a weighted adjustment processing to the model on top of the federated learning model (FedAvg) is proposed. The final experimental results illustrate that the model obtained from the training of this optimisation method has better accuracy compared to the traditional federated learning model.