Transverse federal learning method and device and storage medium

Abstract

The invention provides a transverse federal learning method and device and a storage medium. The method mainly comprises the following steps that: a central server selects an initial federal learning model and parameters and issues the initial federal learning model and parameters to each client; each client simultaneously starts model training based on respective local training data so as to obtain a local learning model; sending a local learning model to a next client for training by adopting a cyclic communication mode among the clients, updating a cyclic communication sequence after all the clients finish training, and continuing to transmit the model and training until a set training frequency is reached; and each client sends the finally trained model to the central server for aggregation, and then the federal learning model is updated by using an aggregation result until the loss function is converged, so that the training is completed. According to the method, when a transmission model or gradient parameters are trained, annular model propagation is firstly carried out, and then star propagation is carried out to a server, so that a new transverse federated learning model training architecture is constructed.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular, to a horizontal federated learning method, device and storage medium. Background technique [0002] As the power and popularity of computing substrates such as phones, sensors, and wearables grow, it is increasingly attractive to learn statistical models locally in a network of distributed devices, rather than moving the data to the data center . This technique is called federated learning. The definition of federated learning is as follows: In the process of machine learning, each participant can use the data of other parties to conduct joint modeling, and all parties do not need to share data resources, that is, when the data is not local, carry out joint data training and establish a shared Machine learning model, and horizontal federated learning is a federated learning algorithm under the condition that the data types of each user are similar but the ...

AI Technical Summary

Problems solved by technology

The ring structure has strong training robustness, and stronger adaptability can be obtained when training on different data sets. However, when a certain user is training, all remaining users need to wait, and the training efficiency is extremely low. In the entire ring network, only the computing power of one machine is being used, resulting in too long training time

Images