基於半監督分散式學習之 AIoT 行動影像分析平台設計與實作

Abstract

意識到那些行動影像的潛在價值，基於邊緣和雲端計算的的一個 行動影像收集平台被推出，嘗試去更有效地利用這些影像。然而，因 為隱私權疑慮跟頻寬的要求，這個平台無法將所有的影像傳回雲端， 基於這個原因，我們想要更完善的去利用這些被留在前端的資料來增 進模型的表現，因此，在這篇論文中，我們為了邊緣計算提出了一個 分散式半監督學習的架構，這個系統不只可以避免使用者上傳他們一 些較敏感的資料，同時也減輕頻寬的需求，我們也使用了現實中的資 料來評估我們的系統，並探討了模型的表現基於一些前端硬體上的限 制。

Recognizing the potential data value of the videos generated from ubiqui- tous personal devices (e.g., dash cams or smartphones), a video collection and analysis platform based on edge/fog and cloud computing is proposed to col- lect and utilize those videos effectively. However, for such a platform, due to the privacy and bandwidth issue, not all of the videos can transmit back to the cloud. We want to fully utilize these left data to increase the model performance further. Thus, in this thesis, we propose a novel distributed ar- chitecture for edge learning, which adopts semi-supervised techniques. The proposed system not only prevents from uploading the sensitive data but also reduce the communication cost. We then evaluate the performance of the sys- tem using real-world video data with a discussion on the performance impact of the hardware limitation at the edge.