Training method and device for federated learning model
A technology of learning models and training methods, applied in the field of data processing, which can solve problems such as the inability to take into account the effective training of federated learning models
Active Publication Date: 2022-05-24
光之树(北京)科技有限公司
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[0004] For this reason, the first purpose of this application is to propose a training method for the federated learning model, which is used to solve the existing federated learning model training methods that cannot effectively train the federated learning model while ensuring that the training nodes Disconnection will not cause technical problems that the model training process cannot be completed smoothly
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[0060] For better understanding of the above technical solutions, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure will be more thoroughly understood, and will fully convey the scope of the disclosure to those skilled in the art.
[0061] The following describes the method and apparatus for training a federated learning model according to the embodiments of the present application with reference to the accompanying drawings.
[0062] figure 1This is a schematic flowchart of a training method for a federated learning model disclosed in an embodiment of the present application. The target node that par...
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The present application discloses a training method and device for a federated learning model. The execution subject is the target node. The method includes: during the training of the federated learning model, obtain the first training node in the offline state, and obtain the first training node from the local cache Obtain the secret share of the alternative intermediate result of the first training node; obtain the secret share of the first loss function of the first training node on the target node according to the secret share of the alternative intermediate result; receive the second intermediate result sent by the remaining participating nodes in the online state According to the secret share of the second intermediate result, the second loss function secret share of the remaining participating nodes on the target node is obtained; according to the first gradient secret share, the second gradient secret share and the loss function of the target node itself, the federation Learn the loss function of the model. Therefore, if the training node goes offline, the target node obtains the secret share of the substitute intermediate result from the local cache to continue training, reducing the impact caused by the node going offline.
Description
technical field [0001] The present application relates to the technical field of data processing, and in particular, to a method and apparatus for training a federated learning model. Background technique [0002] At present, data-driven business innovation is playing a crucial role in promoting the digital transformation of enterprises. In order to break data silos and improve the quality of data use, data cooperation between institutions is becoming more frequent. Federated learning is a feasible solution that can meet privacy protection and data security. Through homomorphic encryption, secret sharing, etc., it ensures that the private data of all parties is not local, and realizes joint computing and modeling. On the other hand, in the process of training the model, the connection state of the training nodes is also an important factor affecting the effectiveness of the model training. Therefore, how to take into account the effective training of the federated learning...
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IPC IPC(8): G06N20/20
CPCG06N20/20
Inventor 夏家骏鲁颖张珣沈敏均陈楚元张佳辰
Owner 光之树(北京)科技有限公司