Improved Asynchronous Federated Learning for Data Injection Pollution

In view of the problems of data pollution, incomplete feature extraction, and poor multi-network parameter sharing and transmission under the federated learning framework of deep learning, this article proposes an improved asynchronous federated learning algorithm of multi-model fusion based on data injection pollution. Through data augmentation, the existing dataset is preprocessed to enhance the algorithm’s ability to identify the noise data. In our approach, the residual network is used to extract the static information of the image, the capsule network is used to extract the spatial dependence among the internal structures of the image, several layers of convolution are used to reduce the dimensions of both features, and the two extracted features are fused. In order to reduce the transmission overhead of parameters shared between the residual network and capsule network, we adopt an asynchronous parameter transmission between the global trainer and the local trainer. When the global trainer broadcasts the parameters to each local trainer, several trainers are randomly selected to avoid communication link blockage. Finally, through conducting various experiments, the results show that our alogrithm can effectively extract the pathological features in the image and achieve higher accuracy, outperforming the current mainstream algorithms.