보안 환경에서 병렬학습된 딥러닝 모형의 통합에 관한 연구
Unification of Deep Learning Model trained by Parallel Learning in Security environment
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초록
최근 인공지능 분야에서 가장 많이 사용하는 딥러닝은 그 구조가 점차 크고 복잡해지고 있다. 딥러닝 모델이 커질수록 이를 학습시키기 위해서는 대용량의 데이터가 필요하지만 데이터가 여러소유 주체별로 분산되어 있고 보안 문제로 인해 이를 통합하여 학습시키기 어려운 경우가 발생한다. 우리는 동일한 딥러닝 모형이 필요하지만 보안 문제로 인해 데이터가 여러곳에 분산되어 처리될 수 밖에 없는 상황에서 데이터를 소유하고 있는 주체별로 분산 학습을 수행한 후 이를 통합하는 방법을 연구하였다. 이를 위해 보안 상황을 V-환경과 H-환경으로 가정하여 소유 주체별로분산학습을 수행했으며 Average, Max, AbsMax를 사용하여 분산학습된 결과를 통합하였다. mnist-fashion 데이터에 이를 적용해 본 결과 V-환경에서는 정확도 면에서 데이터를 통합시켜 학습한 결과와 큰 차이가 없음을 확인할 수 있었으며, H-환경에서는 차이는 존재하지만 의미있는 결과를 얻을 수 있었다.
Abstract
Recently, deep learning, which is the most used in the field of artificial intelligence, has a structure that is gradually becoming larger and more complex. As the deep learning model grows, a large amount of data is required to learn it, but there are cases in which it is difficult to integrate and learn the data because the data is distributed among several owners and security issues. In that situation we conducted parallel learning for each users that own data and then studied how to integrate it. For this, distributed learning was performed for each owner assuming the security situation as V-environment and H-environment, and the results of distributed learning were integrated using Average, Max, and AbsMax. As a result of applying this to the mnist-fashion data, it was confirmed that there was no significant difference from the results obtained by integrating the data in the V-environment in terms of accuracy. In the H-environment, although there was a difference, meaningful results were obtained.
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