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FEDERATED-LEARNING-BASED PERSONAL QUALIFICATION EVALUATION METHOD, APPARATUS AND SYSTEM, AND STORAGE MEDIUM

机译:基于联邦学习的个人资格评估方法,装置和系统,以及存储介质

摘要

A federated-learning-based personal qualification evaluation method, apparatus and system, and a storage medium. The system comprises: a smart terminal (100), an external participant (200), local participants (300) and a central server end (400), wherein the smart terminal (100) performs training on the basis of user behavior data, so as to obtain a first evaluation sub-model; the external participant (200) sends external user data to the central server end, and the central server end (400) performs training on the basis of the external user data, so as to obtain a second evaluation sub-model; and the local participants (300) send gradients of third evaluation sub-models to the central server end (400), and the central server end (400) calculates the weighted average of the obtained gradients to generate an average gradient, and updates model parameters of the third evaluation sub-models on the basis of the average gradient, such that the local participants (300) train a third evaluation model again. The central server end (400) integrates model parameters of the first evaluation sub-model, model parameters of the second evaluation sub-model and model parameters of the third evaluation sub-models to obtain a final global evaluation model.
机译:一种基于联邦学习的个人资格评估方法、装置和系统,以及存储介质。该系统包括:智能终端(100)、外部参与者(200)、本地参与者(300)和中央服务器端(400),其中智能终端(100)基于用户行为数据进行训练,以获得第一评估子模型;外部参与者(200)向中央服务器端发送外部用户数据,中央服务器端(400)基于外部用户数据进行训练,以获得第二评估子模型;本地参与者(300)向中央服务器端(400)发送第三评估子模型的梯度,中央服务器端(400)计算获得的梯度的加权平均值以生成平均梯度,并基于平均梯度更新第三评估子模型的模型参数,这样,本地参与者(300)再次培训第三个评估模型。中央服务器端(400)集成第一评估子模型的模型参数、第二评估子模型的模型参数和第三评估子模型的模型参数,以获得最终的全局评估模型。

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