The invention provides a multi-task
federated learning client contribution degree measurement method based on model layering, and relates to a
federated learning and
machine learning model layering technology. According to the method, a distributed architecture of a central
server and a plurality of
client nodes is included, firstly, a federation learning environment and
data preparation are configured, then, the central
server measures the contribution degree of each
client under an existing federation task, and then, the central
server layers received
model parameters and calculates the gradient of each layer of a model; the model layering similarity relative to the initiator client is calculated to obtain a normalized contribution vector, a new model is obtained through calculation according to the contribution vector and the data size on the client, the client participating in training in the next round is selected, and the task
queue is updated. According to the method, the contribution of each client of federal learning can be accurately identified, more accurate
model aggregation under layered contribution measurement is realized, meanwhile, the
privacy protection effect the same as that of general federal learning is kept, the federal learning time is optimized, and the model precision is improved.