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題名 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作者 張皓博
Chang, Hao-Po貢獻者 張宏慶
Jang, Hung-Chin
張皓博
Chang, Hao-Po關鍵詞 Associated Learning
聯邦學習
分散式學習
邊緣運算
D2D通訊
Associated Learning
Federated Learning
Collaborative Machine Learning,
Mobile Edge Computing
Device-to-Device Communication日期 2023 上傳時間 3-Oct-2023 10:49:01 (UTC+8) 摘要 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.參考文獻 [1] M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, "Deep learning with differential privacy," in Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, 2016, pp. 308-318.[2] T. T. Anh, N. C. Luong, D. Niyato, D. I. Kim, and L.-C. Wang, "Efficient training management for mobile crowd-machine learning: A deep reinforcement learning approach," IEEE Wireless Communications Letters, vol. 8, no. 5, pp. 1345-1348, 2019.[3] Y. Cai and T. Wei, "Efficient Split Learning with Non-iid Data," in 2022 23rd IEEE International Conference on Mobile Data Management (MDM), 2022: IEEE, pp. 128-136.[4] M. Fan, C. Chen, C. Wang, W. Zhou, and J. Huang, "On the Robustness of Split Learning against Adversarial Attacks," arXiv preprint arXiv:2307.07916, 2023.[5] A. Imteaj, U. Thakker, S. Wang, J. Li, and M. H. Amini, "A survey on federated learning for resource-constrained IoT devices," IEEE Internet of Things Journal, vol. 9, no. 1, pp. 1-24, 2021.[6] J. Jeon and J. Kim, "Privacy-sensitive parallel split learning," in 2020 International Conference on Information Networking (ICOIN), 2020: IEEE, pp. 7-9.[7] M. S. Jere, T. Farnan, and F. Koushanfar, "A taxonomy of attacks on federated learning," IEEE Security & Privacy, vol. 19, no. 2, pp. 20-28, 2020.[8] J.-P. Jung, Y.-B. Ko, and S.-H. Lim, "Resource Efficient Cluster-Based Federated Learning for D2D Communications," in 2022 IEEE 95th Vehicular Technology Conference:(VTC2022-Spring), 2022: IEEE, pp. 1-5.[9] T. Li, M. Sanjabi, A. Beirami, and V. Smith, "Fair resource allocation in federated learning," arXiv preprint arXiv:1905.10497, 2019.[10] W. Y. B. Lim et al., "Federated learning in mobile edge networks: A comprehensive survey," IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 2031-2063, 2020.[11] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, "Communication-efficient learning of deep networks from decentralized data," in Artificial intelligence and statistics, 2017: PMLR, pp. 1273-1282.[12] J. Nguyen, K. Malik, H. Zhan, A. Yousefpour, M. Rabbat, M. Malek, and D. Huba, "Federated learning with buffered asynchronous aggregation," in International Conference on Artificial Intelligence and Statistics, 2022: PMLR, pp. 3581-3607.[13] T. Nishio and R. Yonetani, "Client selection for federated learning with heterogeneous resources in mobile edge," in ICC 2019-2019 IEEE international conference on communications (ICC), 2019: IEEE, pp. 1-7.[14] D. Pasquini, G. Ateniese, and M. Bernaschi, "Unleashing the tiger: Inference attacks on split learning," in Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, 2021, pp. 2113-2129.[15] R. Rouil, F. J. Cintrón, A. Ben Mosbah, and S. Gamboa, "Implementation and Validation of an LTE D2D Model for ns-3," in Proceedings of the 2017 Workshop on ns-3, 2017, pp. 55-62.[16] R. A. Rouil. "Public Safety Communications Simulation Tool (ns3 based)." https://www.nist.gov/services-resources/software/public-safety-communications-simulation-tool-ns3-based (accessed Feb. 2021, 2021).[17] J. Ryu, D. Won, and Y. Lee, "A Study of Split Learning Model," in IMCOM, 2022, pp. 1-4.[18] H. S. Sikandar, H. Waheed, S. Tahir, S. U. Malik, and W. Rafique, "A Detailed Survey on Federated Learning Attacks and Defenses," Electronics, vol. 12, no. 2, p. 260, 2023.[19] C. Thapa, P. C. M. Arachchige, S. Camtepe, and L. Sun, "Splitfed: When federated learning meets split learning," in Proceedings of the AAAI Conference on Artificial Intelligence, 2022, vol. 36, no. 8, pp. 8485-8493.[20] V. Turina, Z. Zhang, F. Esposito, and I. Matta, "Federated or split? a performance and privacy analysis of hybrid split and federated learning architectures," in 2021 IEEE 14th International Conference on Cloud Computing (CLOUD), 2021: IEEE, pp. 250-260.[21] P. Vepakomma, O. Gupta, T. Swedish, and R. Raskar, "Split learning for health: Distributed deep learning without sharing raw patient data," arXiv preprint arXiv:1812.00564, 2018.[22] D. Y. Wu, D. Lin, V. Chen, and H.-H. Chen, "Associated Learning: an Alternative to End-to-End Backpropagation that Works on CNN, RNN, and Transformer," in International Conference on Learning Representations, 2021.[23] N. Yoshida, T. Nishio, M. Morikura, K. Yamamoto, and R. Yonetani, "Hybrid-FL for wireless networks: Cooperative learning mechanism using non-IID data," in ICC 2020-2020 IEEE International Conference On Communications (ICC), 2020: IEEE, pp. 1-7.[24] X. Zhang, Y. Liu, J. Liu, A. Argyriou, and Y. Han, "D2D-assisted federated learning in mobile edge computing networks," in 2021 IEEE Wireless Communications and Networking Conference (WCNC), 2021: IEEE, pp. 1-7.[25] Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra, "Federated learning with non-iid data," arXiv preprint arXiv:1806.00582, 2018. 描述 碩士
國立政治大學
資訊科學系
110753113資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110753113 資料類型 thesis dc.contributor.advisor 張宏慶 zh_TW dc.contributor.advisor Jang, Hung-Chin en_US dc.contributor.author (Authors) 張皓博 zh_TW dc.contributor.author (Authors) Chang, Hao-Po en_US dc.creator (作者) 張皓博 zh_TW dc.creator (作者) Chang, Hao-Po en_US dc.date (日期) 2023 en_US dc.date.accessioned 3-Oct-2023 10:49:01 (UTC+8) - dc.date.available 3-Oct-2023 10:49:01 (UTC+8) - dc.date.issued (上傳時間) 3-Oct-2023 10:49:01 (UTC+8) - dc.identifier (Other Identifiers) G0110753113 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/147745 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學系 zh_TW dc.description (描述) 110753113 zh_TW dc.description.abstract (摘要) 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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents 第一章 緒論第一節 研究背景 1第二節 研究動機 1第三節 研究目標 2第四節 論文架構 3第二章 相關研究 4第一節 分散式機器學習 4第二節 聯邦學習與行動邊緣運算 6第三節 拆分學習與聯邦學習混和架構 8第四節 AL模型架構 9第五節 聯邦學習安全性 10第六節 拆分學習安全性 11第三章 研究方法 14第一節 問題分析 14第二節 系統架構 15第三節 挑選參與訓練的邊緣設備 16第四節 邊緣設備分群機制 17第五節 挑選群組內的主邊緣設備 19第六節 拆分並傳送模型 20第七節 Associated Learning (AL)模型聚合 23第八節 系統流程 27第四章 模擬實驗與結果分析 28第一節 實驗模擬環境架構 28第二節 測試方法流程 28第三節 資料處理及資料分割 30第四節 AL模型實作 31第五節 隱私差分實作 35第六節 實驗結果與分析 35第五章 結論與未來研究 47第一節 結論 47第二節 未來研究 48參考文獻 49 zh_TW dc.format.extent 3925769 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110753113 en_US dc.subject (關鍵詞) Associated Learning zh_TW dc.subject (關鍵詞) 聯邦學習 zh_TW dc.subject (關鍵詞) 分散式學習 zh_TW dc.subject (關鍵詞) 邊緣運算 zh_TW dc.subject (關鍵詞) D2D通訊 zh_TW dc.subject (關鍵詞) Associated Learning en_US dc.subject (關鍵詞) Federated Learning en_US dc.subject (關鍵詞) Collaborative Machine Learning, en_US dc.subject (關鍵詞) Mobile Edge Computing en_US dc.subject (關鍵詞) Device-to-Device Communication en_US dc.title (題名) 基於Associated Learning架構優化MEC環境訓練模型之效能 zh_TW dc.title (題名) Optimize the Performance of the Training Model in the MEC Environment based on the Associated Learning Architecture en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, "Deep learning with differential privacy," in Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, 2016, pp. 308-318.[2] T. T. Anh, N. C. Luong, D. Niyato, D. I. Kim, and L.-C. Wang, "Efficient training management for mobile crowd-machine learning: A deep reinforcement learning approach," IEEE Wireless Communications Letters, vol. 8, no. 5, pp. 1345-1348, 2019.[3] Y. Cai and T. Wei, "Efficient Split Learning with Non-iid Data," in 2022 23rd IEEE International Conference on Mobile Data Management (MDM), 2022: IEEE, pp. 128-136.[4] M. Fan, C. Chen, C. Wang, W. Zhou, and J. Huang, "On the Robustness of Split Learning against Adversarial Attacks," arXiv preprint arXiv:2307.07916, 2023.[5] A. Imteaj, U. Thakker, S. Wang, J. Li, and M. H. Amini, "A survey on federated learning for resource-constrained IoT devices," IEEE Internet of Things Journal, vol. 9, no. 1, pp. 1-24, 2021.[6] J. Jeon and J. Kim, "Privacy-sensitive parallel split learning," in 2020 International Conference on Information Networking (ICOIN), 2020: IEEE, pp. 7-9.[7] M. S. Jere, T. Farnan, and F. Koushanfar, "A taxonomy of attacks on federated learning," IEEE Security & Privacy, vol. 19, no. 2, pp. 20-28, 2020.[8] J.-P. Jung, Y.-B. Ko, and S.-H. Lim, "Resource Efficient Cluster-Based Federated Learning for D2D Communications," in 2022 IEEE 95th Vehicular Technology Conference:(VTC2022-Spring), 2022: IEEE, pp. 1-5.[9] T. Li, M. Sanjabi, A. Beirami, and V. Smith, "Fair resource allocation in federated learning," arXiv preprint arXiv:1905.10497, 2019.[10] W. Y. B. Lim et al., "Federated learning in mobile edge networks: A comprehensive survey," IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 2031-2063, 2020.[11] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, "Communication-efficient learning of deep networks from decentralized data," in Artificial intelligence and statistics, 2017: PMLR, pp. 1273-1282.[12] J. Nguyen, K. Malik, H. Zhan, A. Yousefpour, M. Rabbat, M. Malek, and D. Huba, "Federated learning with buffered asynchronous aggregation," in International Conference on Artificial Intelligence and Statistics, 2022: PMLR, pp. 3581-3607.[13] T. Nishio and R. Yonetani, "Client selection for federated learning with heterogeneous resources in mobile edge," in ICC 2019-2019 IEEE international conference on communications (ICC), 2019: IEEE, pp. 1-7.[14] D. Pasquini, G. Ateniese, and M. Bernaschi, "Unleashing the tiger: Inference attacks on split learning," in Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, 2021, pp. 2113-2129.[15] R. Rouil, F. J. Cintrón, A. Ben Mosbah, and S. Gamboa, "Implementation and Validation of an LTE D2D Model for ns-3," in Proceedings of the 2017 Workshop on ns-3, 2017, pp. 55-62.[16] R. A. Rouil. "Public Safety Communications Simulation Tool (ns3 based)." https://www.nist.gov/services-resources/software/public-safety-communications-simulation-tool-ns3-based (accessed Feb. 2021, 2021).[17] J. Ryu, D. Won, and Y. Lee, "A Study of Split Learning Model," in IMCOM, 2022, pp. 1-4.[18] H. S. Sikandar, H. Waheed, S. Tahir, S. U. Malik, and W. Rafique, "A Detailed Survey on Federated Learning Attacks and Defenses," Electronics, vol. 12, no. 2, p. 260, 2023.[19] C. Thapa, P. C. M. Arachchige, S. Camtepe, and L. Sun, "Splitfed: When federated learning meets split learning," in Proceedings of the AAAI Conference on Artificial Intelligence, 2022, vol. 36, no. 8, pp. 8485-8493.[20] V. Turina, Z. Zhang, F. Esposito, and I. Matta, "Federated or split? a performance and privacy analysis of hybrid split and federated learning architectures," in 2021 IEEE 14th International Conference on Cloud Computing (CLOUD), 2021: IEEE, pp. 250-260.[21] P. Vepakomma, O. Gupta, T. Swedish, and R. Raskar, "Split learning for health: Distributed deep learning without sharing raw patient data," arXiv preprint arXiv:1812.00564, 2018.[22] D. Y. Wu, D. Lin, V. Chen, and H.-H. Chen, "Associated Learning: an Alternative to End-to-End Backpropagation that Works on CNN, RNN, and Transformer," in International Conference on Learning Representations, 2021.[23] N. Yoshida, T. Nishio, M. Morikura, K. Yamamoto, and R. Yonetani, "Hybrid-FL for wireless networks: Cooperative learning mechanism using non-IID data," in ICC 2020-2020 IEEE International Conference On Communications (ICC), 2020: IEEE, pp. 1-7.[24] X. Zhang, Y. Liu, J. Liu, A. Argyriou, and Y. Han, "D2D-assisted federated learning in mobile edge computing networks," in 2021 IEEE Wireless Communications and Networking Conference (WCNC), 2021: IEEE, pp. 1-7.[25] Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra, "Federated learning with non-iid data," arXiv preprint arXiv:1806.00582, 2018. zh_TW
