The invention provides a federal learning dynamic
client selection and
privacy protection method based on a dobby
machine and fuzzy calculation, and aims to improve the
client selection efficiency and the
privacy protection effect. According to the method, clients are dynamically selected through a UCB strategy in a dobby
machine algorithm, rewards are calculated according to real-time performance (such as accuracy, training loss and the like) of each
client, and updating is carried out in the training process. In combination with a fuzzy calculation technology, a
Gaussian membership function is adopted to carry out fuzzy
processing on uploaded
model parameters, so that privacy is effectively protected, and the risk of data leakage is reduced. By introducing a dynamic adjustment factor, the method can balance the proportion of
noise to
original data, and further enhance
privacy protection. Experimental results show that the client
selection strategy based on the UCB shows relatively high training efficiency and model performance on MNIST and CIFAR-10 data sets, and is obviously superior to a traditional
federated learning method. The method not only optimizes the client
selection strategy, but also reduces the calculation and communication overhead, improves the practicability and adaptability of federal learning, and especially can effectively improve the training efficiency and privacy protection capability in a large-scale
system.