VAE-based medical care federated learning framework determination method

Abstract

The invention discloses a VAE-based medical care federated learning framework determination method, relates to the technical fields of computer science, machine learning and federated learning, and solves the three challenges in the federated learning-based medical care field: 1, the problems of final model performance reduction and slow convergence rates caused by non-IID data distribution; secondly, the problem of large model deviations caused by unbalanced distribution of positive and negative samples of medical data; and thirdly, the problem of difficult actual deployment caused by too large communication bandwidths consumed under federated learning. According to the invention, two lightweight VAEs are trained under a federated learning framework; then the trained VAEs are distributed to all nodes to be used for updating local data of the nodes, wherein updated local data has the characteristics that all the data is low in dimension, distribution is similar, and categories are balanced; and finally, an anomaly detection model is trained by using the data.

Description

technical field [0001] The present invention relates to the field of computer science, more specifically to the technical field of determination methods based on the VAE healthcare federated learning framework. Background technique [0002] In recent years, the advent of wearable technology has improved patients' lives and treatment experiences. Wearables and mobile devices are fundamentally changing the way we approach healthcare. Remote Patient Monitoring (RPM) is an implementation of the Internet of Medical Things (IoMT) that helps in delivering high-quality care and timely remote intervention to avert health crises. In order to obtain a high-quality remote anomaly detection system (such as abnormal heart rate detection, fall detection, etc.), it must be learned through a large amount of data. However, data in the medical field is very sensitive and private, which means that data between medical institutions cannot be shared. Therefore, unlike the traditional method of ...

AI Technical Summary

Problems solved by technology

[0003] Although federated learning is a very promising distributed learning framework, it still has many problems and challenges

The first problem is that the data distribution of each node under distributed training is not independent and identically distributed (non-IID), which will lead to two problems: the convergence of the global model will be very slow and the final global model effect will be Much less effective than centralized learning models

The second problem is the problem of data imbalance (imbalanced), which is particularly prominent in the field of healthcare, because abnormal data is much less likely to occur than normal data, such as in the data of fall detection, the data of daily activities Much more data than fall types (since people are doing most of their daily activities rather than falling)

And the result of this will be that the model will pay more attention to the data with many categories to get a biased model, which will affect the final model accuracy.

The third problem is that the communication cost of federated learning framework is too high

Although some studies have focused on solving a certain problem in the statistical challenges of federated learning and achieved good results, if they are simply put together, the result is that the privacy of users is violated and the cost of computing is increased. Some programs are difficult to be compatible

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