Abstract: Provided are a correlation coefficient acquisition method, an electronic device, and a non-transitory computer readable storage medium. The implementation scheme is as follows: first original data is acquired, the first original data is homomorphically encrypted by using an associated key to determine first transmission data, where the associated key is jointly agreed by the first participation node and a second participation node; the first transmission data is sent to an auxiliary node so that the auxiliary node receives the first transmission data and performs a homomorphic operation on the first transmission data and second transmission data to obtain correlation coefficients between the first original data and second original data, where the second transmission data is determined by the second participation node homomorphically encrypting the second original data by using the associated key; and the correlation coefficients fed back by the auxiliary node is received.

Abstract: A federated learning method and apparatus, a device and a medium are provided, and relates to the field of artificial intelligence, in particular to the field of federated learning and machine learning. The federated learning method includes: receiving data related to a federated learning task of a target participant, wherein the target participant at least includes a first computing device for executing the federated learning task; determining computing resources of the first computing device that are able to be used to execute the federated learning task; and generating a first deployment scheme for executing the federated learning task in response to determining that the data and the computing resources meet a predetermined condition, wherein the first deployment scheme instructs to generate at least a first work node and a second work node on the first computing device.

Abstract: A federated learning method and system, an electronic device, and a storage medium, which relate to a field of artificial intelligence, in particular to fields of computer vision and deep learning technologies. The method includes: performing a plurality of rounds of training until a training end condition is met, to obtain a trained global model; and publishing the trained global model to a plurality of devices. Each of the plurality of rounds of training includes: transmitting a current global model to at least some devices in the plurality of devices; receiving trained parameters for the current global model from the at least some devices; performing an aggregation on the received parameters to obtain a current aggregation model; and adjusting the current aggregation model based on a globally shared dataset, and updating the adjusted aggregation model as a new current global model for a next round of training.