模型状态机生成方法、装置、电子设备和计算机可读介质
By generating a model state machine, the complex state transition problem of federated learning models under multiple states, types, scenarios, and dimensions is solved, improving code maintainability and state transition accuracy, and ensuring the training and prediction effects of federated learning models.
CN117610686BActive Publication Date: 2026-07-17JINGDONG TECH HLDG CO LTD
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- JINGDONG TECH HLDG CO LTD
- Filing Date
- 2023-12-20
- Publication Date
- 2026-07-17
AI Technical Summary
Technical Problem
Federated learning models have complex state transition management under multiple states, types, scenarios, and dimensions, resulting in poor code maintainability and difficulty in detection and modification.
Method used
By generating a model state machine, based on the business scenario division and state transition conditions of the federated learning model, a state machine is created, and the SPI mechanism is used to adjust and call the state machine to generate the model state machine of the federated learning model.
Benefits of technology
It simplifies the management of state transitions in complex models, improves code maintenance efficiency and the accuracy of state transitions, and ensures the training and prediction performance of federated learning models.
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Figure CN117610686B_ABST
Abstract
本公开的实施例公开了模型状态机生成方法、装置、电子设备和计算机可读介质。该数据确定方法的一具体实施方式包括：基于联邦学习模型中多个业务场景的划分，确定待创建的状态机数量；根据多个业务场景中每个业务场景的处理流程，确定联邦学习模型在该业务场景中涉及的状态，以及根据各状态之间的转移条件，创建该业务场景下的状态机；根据多个业务场景之间的关系，确定创建的多个状态机之间的调用关系，生成联邦学习模型的模型状态机。该实施方式与联邦学习技术有关，可以解决复杂模型状态流转问题和代码可维护性问题，有助于提升代码的维护效率。
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