Beijing Baidu Netcom Science and Technology Co., Ltd.

Yangjie Zhou

Yan Fu

Lianghui Chen

Jun Fang

Lu Gan

Shunnan Xu

A method includes: sending, by one or more computers, in response to the number of data providers for federated learning being greater than a first threshold, a data field required for the federated learning to a coordinator, the coordinator comprising a computer; receiving, by one or more computers, from the coordinator, information about one or more data providers comprising the required data field, for determining the data providers comprising the required data field as the remaining data providers, wherein the coordinator stores a data field of each data provider; and performing, by one or more computers, federated learning-based modeling with each of the remaining data providers.

Federated learning for improving matching efficiency

The disclosure provides an image clustering method and an image clustering apparatus. The method includes: obtaining new images, and clustering the new images to obtain a first cluster; determining a historical cluster similar to the first cluster as a second cluster from existing historical clusters; obtaining a distance between the first cluster and the second cluster; and generating a target cluster by fusing the first cluster and the second cluster based on the distance. In the image clustering method, with the image clustering apparatus of the disclosure, secondary clustering processing performed on the existing historical clusters based on newly added images is not required, new and old clusters are directly fused.

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Beijing, China

Yangjie Zhou