2results about How to "Reduce convergence" patented technology

The invention discloses an acne-removing essence and a preparation method thereof, and particularly relates to the technical field of skin care products, the acne-removing essence comprises the following components by mass: 0.5-5% of salicylic acid; 1%-5% of tartaric acid; 2%-6% of nicotinamide; 0.5%-2% of colloidal sulfur; 1.5%-3% of tea tree essential oil; 1%-2% of hyaluronic acid; 0.5%-1% of ceramide; 1%-3% of glycerol; 1%-2% of trehalose; the salicylic acid is used for penetrating into skin, helping to relieve dead skin cells, reducing inflammation and accelerating skin renewal; the tartaric acid is used for realizing a stripping effect, can remove aged cuticles on the surface of the skin and promote the generation of new cells, so that the problem of acnes is helped to be improved. The acne-removing essence prepared by the invention has a relatively good moisturizing effect, so that skin barrier can be well maintained, keratin metabolism is promoted, grease secretion is inhibited, acne is effectively prevented, inflammatory response of the acne is relieved, and the actual use effect of the acne-removing essence is relatively good.

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.