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An asynchronous federated learning aggregation method and system based on clustering cache

ActiveCN122087484BAvoid global aggregation delaysGuaranteed real-time receptionEngineeringData mining
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.
Owner:HEFEI UNIV OF TECH