Target recognition federal deep learning method based on trusted network

• Main Authors: ZHENG YIZE, YANG JUAN
• Format: Patent
• Language: Chinese; English
• Published: 11.06.2021
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; HANDLING RECORD CARRIERS; PHYSICS; PRESENTATION OF DATA; RECOGNITION OF DATA; RECORD CARRIERS

The invention discloses a target recognition federated deep learning method based on a trusted network. The method is mainly composed of a local model and a federated model. The local model and the federated model have the same structure, the local model and the federated model are trained by adopting the same optimization algorithm (such as an Adam optimizer) and passing training parameters (such as a learning rate eta, a neural network weight w, a loss function E and the like), the local model and the federated model jointly train a convolutional neural network by adopting a federated learning mode, training data of each client is local, and the data is immobile and the model is mobile. The highest recognition precision of the method can reach 91%, and the method has the advantages of being high in recognition precision and high in convergence speed. Through the method, the problems of difficulty in data fusion, long decision response time and the like in cross-client fields can be solved, the decision time