The present application relates to the technical field of
artificial intelligence and federated
machine learning, and particularly relates to an asynchronous
federated learning aggregation method and
system based on clustering cache. The present application clusters clients by intermediate features of the clients. In global aggregation, intra-cluster aggregation is firstly performed, and then global aggregation is performed based on
client clusters. The
client clusters are dynamically updated. In intra-cluster aggregation, an active set and a slow set are introduced to realize an asynchronous participation mechanism of intra-cluster aggregation. Weighted aggregation of the active set and the slow set solves the problem of
system heterogeneity. The present application solves the defect of insufficient timeliness of existing
federated learning methods in a data heterogeneous scene, and guarantees the timeliness and model training accuracy of
federated learning in the data heterogeneous scene.