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    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/147745
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/147745

    Title: 基於Associated Learning架構優化MEC環境訓練模型之效能
    Optimize the Performance of the Training Model in the MEC Environment based on the Associated Learning Architecture
    Authors: 張皓博
    Chang, Hao-Po
    Contributors: 張宏慶
    Jang, Hung-Chin
    Chang, Hao-Po
    Keywords: Associated Learning
    Associated Learning
    Federated Learning
    Collaborative Machine Learning,
    Mobile Edge Computing
    Device-to-Device Communication
    Date: 2023
    Issue Date: 2023-10-03 10:49:01 (UTC+8)
    Abstract: 近年來,隨著行動通訊網路的進步,邊緣設備的數量及運算能力提升,再加上人工智慧的蓬勃發展,以及資料隱私意識的抬頭,催生出運用邊緣設備訓練模型的分散式機器學習,其中包括聯邦學習以及拆分學習,然而這兩種方法在架構上存在明顯的優缺點。本研究旨在提出一個訓練架構,與聯邦學習相比,不僅能達到相似的模型準確度,同時在訓練過程中也能減少邊緣設備的運算量以及降低邊緣伺服器的流量,並且改善使用模型時的延遲,進一步提升使用者體驗。為了實現這一目標,在系統架構中採用兩層式設計,提出一個啟發式的分群演算法,群組內各邊緣設備只訓練部分模型,邊緣設備間使用設備到設備通訊技術,利用Associated Learning架構來解決拆分模型後反向傳播的流量問題,此外群組內僅透過主設備與邊緣伺服器通訊,進一步降低了邊緣伺服器的流量負擔。為了驗證本研究是否有達成預期指標,模擬實驗中採用PyTorch及ns3進行模擬,從實驗結果可以驗證本研究相較於聯邦學習在實驗中有更佳的準確度,且透過Associated Learning特色能降低使用時的延遲,提升使用者體驗,針對特定情況下也能夠降低邊緣設備運算量及邊緣伺服器流量,最後提出本研究可優化之部分,並歸納出未來學者可持續往安全性、更通用的架構、更合乎現實情況的模擬等方向研究。
    In recent years, with the advancement of cellular networks, the number and computing power of edge devices have increased. The vigorous development of artificial intelligence and the rise of data privacy awareness have spawned distributed machine learning that uses edge device training models, including federated learning and split learning. However, both have obvious advantages and disadvantages in terms of architecture. The purpose of this study is to propose a training framework. Compared with federated learning, it can not only achieve similar model accuracy but also reduce the computation of edge devices and the traffic of edge server during the training process, improve the latency when using the model, and further enhances the user experience. Therefore, a heuristic grouping algorithm is proposed, and a two-layer design is adopted in the system architecture. Each edge device in the group only trains parts of the model and communications through Device-to-Device. The Associated Learning architecture is used to decouple the dependency relationship of backpropagation when updating the model parameters, and it is expected to reduce the amount of computational required to train the model. After grouping, the multi-objective function is used to select the master edge device, and the group only communicates with the edge server through the master edge device, which is expected to reduce the traffic of the edge server. To verify whether this study has achieved the expected indicators, PyTorch and ns3 are used to simulate the experiment. According to experimental results, it can be verified that this study has better accuracy than federated learning in the experiment. Through the Associated Learning feature, it can reduce the latency during inference, improve the user experience, and reduce the computing load of edge devices and the traffic of edge servers under certain circumstances. Finally, the part of this research that can be optimized is proposed, and the sustainable research directions of future scholars are summarized, including security, more general architecture, and more realistic simulation.
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110753113
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

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