Federated Learning-Based Model Management System

Abstract

A federated learning-based model management system includes a server-side device and multiple user-side devices. The server-side device stores multiple user accounts corresponding to each user-side device and multiple artificial intelligence (AI) models. Each AI model has a corresponding model number, and each user-side account includes multiple usage permissions corresponding to that model number. Upon receiving a login request from a target user-side device regarding a user account to be verified, the server-side device verifies the user account, which is one of the user-side accounts. If verification is successful, the target server-side device authorizes the target user-side device to use the corresponding AI model based on the usage permissions for each model number.

Claims

1. A model management system based on federated learning, comprising: a third-party computing unit; and at least one model management unit, each model management unit being signal-connected to each other, each model management unit comprising: A server-side device is connected to the third-party computing unit. Each server-side device stores a training dataset, multiple user accounts, multiple user passwords corresponding to the user accounts, and multiple trained artificial intelligence models. Each artificial intelligence model has a model number, and each user account includes multiple usage permissions corresponding to the model number. There are also multiple user-side devices connected to the server-side device and corresponding to the user accounts.In this system, a target server device within a target model management unit, upon receiving a login request from a target user device containing a user account and a user password to be verified, determines whether the user password corresponding to the user account to be verified is the same as the user password to be verified. The target model management unit is one of the at least one model management unit, the target server device is the server device within the target model management unit, the target user device is one of the user devices within the target model management unit, and the user account to be verified is one of the user accounts stored by the target server device. When the target server device determines that the user password corresponding to the user account to be verified is the same as the user password to be verified, for each AI model stored by the target server device, the target server device determines whether the usage permissions corresponding to the model number included in the user account to be verified indicate that the AI ​​model corresponding to the model number can be used. When the target server determines that the usage permission corresponding to the model number included in the user account to be verified indicates that the user can use the artificial intelligence model corresponding to the model number, the target server authorizes the target user to use the artificial intelligence model corresponding to the model number. After receiving a model training request containing an algorithm from the target user, the target server generates and transmits a model training requirement containing the algorithm to other server devices and the third-party computing unit according to the model training request. Based on its stored training dataset, the target server uses the algorithm to generate and transmit multiple model adjustment parameters to the third-party computing unit. For each server device other than the target server, the server generates and transmits multiple other model adjustment parameters to the third-party computing unit based on its stored training dataset and the algorithm. After receiving the model adjustment parameters transmitted by all server devices, the third-party computing unit trains a target artificial intelligence model corresponding to the algorithm based on the model adjustment parameters and the algorithm. After the third-party computing unit completes training and obtains the target AI model, it transmits the target AI model to the target server device. Upon receiving the target AI model, the target server device sets the usage permissions for the target AI model in the user account corresponding to the target user device, thereby authorizing the target user device to use the target AI model.

2. The federated learning-based model management system as described in claim 1, wherein, Each model management unit also includes a management terminal device that is signal-connected to the server device. The server device also stores a management terminal account corresponding to the management terminal device and a management terminal password corresponding to the management terminal account. After receiving another login request from a target management terminal device that includes a management terminal account to be verified and a management terminal password to be verified, the target server device determines whether the management terminal password is the same as the management terminal password to be verified. The target management terminal device is the management terminal device in the target model management unit. When the target server device determines that the management terminal password is the same as the management terminal password to be verified, it generates and sends a management terminal login success message indicating that the target management terminal device has successfully logged in to the target management terminal device.

3. The federated learning-based model management system as described in claim 2, wherein, After receiving a permission adjustment request from the target management device, which includes a user account to be adjusted and a model number to be adjusted, the target server device adjusts the usage permission of the model number to be adjusted in the user account to be adjusted according to the permission adjustment request. The user account to be adjusted is one of the user accounts, and the model number to be adjusted is one of the model numbers corresponding to the artificial intelligence models stored in the target server device.

4. The federated learning-based model management system as described in claim 1, wherein, After receiving a model usage request from the target user device that includes data to be analyzed and a model number to be used, the target server device generates and stores a log file about the target user device using the artificial intelligence model corresponding to the model number to be used. The artificial intelligence model corresponding to the model number to be used includes one of the artificial intelligence models corresponding to all model numbers that the target server device has authorized the target user device to use.

5. The federated learning-based model management system as described in claim 1, wherein, The model training request also includes at least one specified user account. The model training requirement also includes the at least one specified user account. The at least one specified user account is at least one of the user accounts stored in the server devices. After the third-party computing unit completes the training and obtains the target artificial intelligence model, it transmits the target artificial intelligence model to the server devices. For each specified user account, after receiving the target artificial intelligence model, the server device storing the specified user account sets the usage permissions of the specified user account for the target artificial intelligence model, so as to authorize the user device corresponding to the specified user account to use the target artificial intelligence model.

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