Model training method and device based on longitudinal federated learning system and storage medium

Abstract

The embodiment of the invention discloses a model training method and device based on a longitudinal federated learning system and a storage medium. The method comprises: executing an objective function of a model to be trained, wherein the model to be trained comprises at least two types of model parameter sets, and each model parameter set corresponds to a matched training data set and / or training data label set; analyzing each data item included in the target function layer by layer to obtain a logic plan execution tree; generating a physical execution plan according to the training data set and / or the training data label set used by each tree node in the logic plan execution tree; and according to the physical execution plan, scheduling each device in the longitudinal federated learning system to train each model parameter set included in the to-be-trained model. According to the scheme of the embodiment of the invention, the model training process of longitudinal federation learning does not need to be customized and developed manually, and the model training process based on a longitudinal federation learning system is simplified.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of artificial intelligence, and in particular to a model training method, device and storage medium based on a longitudinal federated learning system. Background technique [0002] With the development and large-scale application of big data and artificial intelligence technology, enterprises and related departments are paying more and more attention to data security and data privacy protection. Federated learning is an emerging artificial intelligence technology. Its design goal is to carry out efficient learning among multiple devices (participants) or multiple computing nodes under the premise of ensuring data security, protecting data privacy, and ensuring legal compliance. machine learning. [0003] Federated learning can be divided into horizontal federated learning and vertical federated learning. Among them, vertical federated learning needs to decompose the calculation (mo...

AI Technical Summary

Problems solved by technology

[0003] Federated learning can be divided into horizontal federated learning and vertical federated learning. Among them, vertical federated learning needs to decompose the calculation (model training) process according to the characteristics of data distribution and cooperate with each device; therefore, the calculation process of vertical federated learning algorithm , it needs to be customized and developed manually according to the characteristics of the algorithm and data, which makes the current model training work based on the vertical federated learning framework (system) more cumbersome

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