Model training method based on federated learning

A model training and federation technology, applied in the information field, can solve the problem that the model parameters are not suitable for exposed nodes and so on

Active Publication Date: 2020-04-03
ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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[0005] However, in some scenarios, model para

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  • Model training method based on federated learning
  • Model training method based on federated learning
  • Model training method based on federated learning

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[0028] Under the framework of federated learning, the server is usually responsible for updating model parameters according to the gradient uploaded by the node, and sending the model parameters to the node, and the node calculates the gradient based on the model parameters and local training samples. In order to prevent the server from inferring the local training samples of the node based on the gradient uploaded by the node, the node uploads the gradient to the server based on the SA protocol, so that the server only obtains the sum of the gradients uploaded by each node, but cannot obtain it. The gradient uploaded by a single node.

[0029] It can be seen that under the existing federated learning architecture, nodes can hide local training samples from the server, but the server will not hide model parameters from the node.

[0030] However, in some scenarios, the server does not want to expose privacy (ie model parameters) to nodes. For example, suppose that a fraudulent tra...

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Abstract

The invention discloses a model training method based on federated learning. In order to protect privacy (model parameters) of a server from being leaked, a server adopts a homomorphic encryption algorithm to encrypt a model parameter set and then issues the encrypted model parameter set to a node, and the node performs model calculation in an encrypted state by using the encrypted model parameters and a local training sample based on a homomorphic encryption principle to obtain an encryption gradient. Subsequently, the node calculates a difference between the encryption gradient and the encryption random number based on the homomorphic encryption principle, this difference being substantially a meaningless value of the encryption. And then, the node uploads the encrypted value to a server. Furthermore, the server can acquire the sum of the random numbers on each node by utilizing an SA protocol on the premise of not acquiring the random number on each node. Thus, the server can restore the sum of gradients generated by each node according to the sum of the encrypted value uploaded by each node and each random number, so that model parameters can be updated.

Description

technical field [0001] The embodiment of this specification relates to the field of information technology, and in particular to a model training method based on federated learning. Background technique [0002] Federated learning (Federated machine learning / Federated Learning) refers to a machine learning framework that can effectively help multiple nodes (which can represent individuals or institutions) jointly train models while meeting the requirements of data privacy protection. [0003] Under the framework of federated learning, the server sends model parameters to multiple nodes, and each node inputs local training samples into the model for a training session. After the training, each node will calculate the gradient based on the training results . Subsequently, the server can calculate the sum of the gradients of each node based on the Secure Aggregation (SA, Secure Aggregation) protocol. It is worth emphasizing that the server is restricted by the SA protocol and...

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Application Information

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IPC IPC(8): G06F21/60G06F21/62G06N20/00
CPCG06F21/602G06F21/6245G06N20/00
Inventor 王力陈超超周俊
Owner ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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