Research on efficient data processing method for fog computing based on blockchain and federated learning

Abstract

Federated learning provides an effective distributed machine learning solution for "centralized" processing of decentralized data. However, the increasing size of data and complexity of learning models in the context of digitalization have placed higher demands on the computing power of training equipment. To address the above problems, a Fog Computing Blockchain Federated Learning (FC-BCFL) method is proposed to realize secure and efficient processing of massive data. Firstly, an incentive mechanism based on time reputation value and loss reputation value is designed with the help of blockchain to motivate clients with high-quality data to join the training and improve the accuracy of the model. Secondly, a fog node selection mechanism based on time reputation value is proposed and designed to select efficient fog nodes to train local models at the edge of the network using the perturbed data from clients processed by the local differential privacy technique in order to shorten the training time of federated learning and improve the training efficiency. In addition, a model aggregation LR-FedAvg algorithm is designed in this method, which weights the local model update parameters according to the loss reputation value to increase the weight of the high-precision local parameters in the global parameters so as to reduce the number of model training times and get the converged global optimal model faster. Finally, comparative experiments were conducted on the MNIST dataset, Fashion MNIST dataset, and CIFAR-10 dataset for three variables: training rounds, one iteration time, and total training time. It was concluded that the FC-BCFL method has been further optimized and improved in terms of model global accuracy, training rounds, and training time. This shows that the model can learn efficiently while ensuring data privacy, and a model with higher accuracy can be obtained with relatively fewer training rounds and iteration time. This verifies the effectiveness of the proposed FC-BCFL method.