FEDERATED LEARNING METHOD AND APPARATUS, AND CHIP

    公开(公告)号:US20230116117A1

    公开(公告)日:2023-04-13

    申请号:US18080523

    申请日:2022-12-13

    Abstract: A method includes: A second node sends a prior distribution of a parameter in a federated model to at least one first node. After receiving the prior distribution of the parameter in the federated model, the at least one first node performs training based on the prior distribution of the parameter in the federated model and local training data of the first node, to obtain a posterior distribution of a parameter in a local model of the first node. After the local training ends, the at least one first node feeds back the posterior distribution of the parameter in the local model to the second node, so that the second node updates the prior distribution of the parameter in the federated model based on the posterior distribution of the parameter in the local model of the at least one first node.

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