The invention provides a user position prediction framework based on clustered graph federal learning. The user position prediction framework comprises the following steps that S1, using a
sequence prediction model to carry out training locally by users; S2, uploading the
model parameters and the implicit state of the original sequence data passing through an
encoder to a
server by users; S3, learning a similar graph structure by using an implicit state; S4, obtaining an embedded representation of the users through a graph
convolutional neural network; S5, dividing the users into a plurality of clusters through a clustering method, and executing a federated average
algorithm by the users in each cluster; and S6, downloading the embedded representation and the averaged
model parameters to the corresponding users, splicing the implicit state and the embedded representation by each user, then outputting a prediction result, and updating the
server model parameters. The method has the advantages that federal learning protects data privacy; the graph convolutional network solves the problem of insufficient training cost caused by
label scarcity; and the graph clustering
algorithm enables more similar users to execute a federated average
algorithm so as to solve the problem of heterogeneity between the users.