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| Title | 基於命名式網路架構之個性化聯邦推薦系統 Personalized Federated Recommendation Systems Via Named Data Networking |
| Creator | 蔡明衡 Tsai, Ming-Heng |
| Contributor | 蔡子傑 Tsai, Tzu-Chieh 蔡明衡 Tsai, Ming-Heng |
| Key Words | 推薦系統 聯邦學習 個性化聯邦學習 命名資料網路 點對點分散式網路 資料隱私 Recommender system Federated Learning Personalized Federated Learning Named Data Networking Peer-to-peer Data Privacy |
| Date | 2024 |
| Date Issued | 4-Feb-2025 15:43:41 (UTC+8) |
| Summary | 隨著資訊技術和人工智慧的持續進步，個人智慧設備的發展促使資料分析和
隱私保護的重要性逐漸增加。聯邦學習作為一種新型的機器學習架構，不僅能夠滿足資料隱私的需求，允許分散的資料保持在原始位置，同時還能進行模型的協同訓練。但是聯邦學習在資料非獨立同分佈（Non-IID）情境下，仍面臨諸多挑戰。而個性化聯邦學習是一種有前景的解決方案。本研究聚焦在個性化學習中的客戶端選擇與多中心聯邦學習上面。
本研究以不同網路架構對聯邦學習進行了改良和詳細探討。在 NDN 架構中
進行客戶端選擇可以以更少的網路成本達到較好的效能；而在 P2P 多中心聯邦學習中，雖然需要提高網路成本，但能夠獲得更好的結果。將這兩者結合後，可以同時達到更低的網路成本和較佳的推薦效能。此外，本研究還採用了元學習的概念，在面對推薦系統中新加入的用戶時，取得了良好的結果。
綜上所述，本研究不僅深入探討了個性化聯邦學習所面臨的各種挑戰，還
提出了多種基於網路架構的優化策略，尤其是基於 NDN 網路的客戶端選擇方法，顯著降低聯邦學習的通訊成本並提升推薦效能。這些研究成果對理解和優化聯邦學習具有重要的參考價值，並為未來的研究和實際應用提供了概念性驗證。 With the continuous advancement of information technology and artificial intelligence, the development of personal smart devices has emphasized the growing importance of data analysis and privacy protection. Federated learning, as a novel machine learning framework, not only satisfies data privacy requirements by allowing distributed data to remain at their original locations but also enables collaborative model training. However, federated learning faces numerous challenges, particularly under non-independent and identically distributed (Non-IID) data scenarios. Personalized federated learning emerges as a promising solution to address these challenges. This study focuses on client selection and multi-center federated learning within the scope of personalized learning. This research explores and improves federated learning through various network architectures. Client selection within the NDN architecture achieves better performance with reduced network costs, while P2P multi-center federated learning, though incurring higher network costs, delivers superior results. By combining these two approaches, the system achieves lower network costs and improved recommendation performance. Furthermore, the study adopts the concept of meta-learning, achieving favorable results when addressing newly added users in the recommendation system. In conclusion, this study not only delves into the challenges faced by personalized federated learning but also proposes several optimization strategies based on network architectures, particularly the NDN-based client selection method, to significantly reduce communication costs and enhance recommendation performance. These findings provide valuable insights for understanding and optimizing federated learning and offer conceptual validation for future research and practical applications |
| 參考文獻 | [1] Konečný, J., McMahan, H. B., Ramage, D., & Richtárik, P. (2016). Federated optimization: Distributed machine learning for on-device intelligence. arXiv preprint arXiv:1610.02527. [2] Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 1-19. [3] McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017, April). Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics (pp. 1273-1282). PMLR. [4] Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37. [5] Mohammadi, M. (2020). A new non-negative matrix factorization method to build a recommender system. Journal of Research in Science, Engineering and Technology, 8(2), 6-12. [6] Sun, Z., Xu, Y., Liu, Y., He, W., Kong, L., Wu, F., ... & Cui, L. (2022). A survey on federated recommendation systems. arXiv preprint arXiv:2301.00767. [7] Tan, A. Z., Yu, H., Cui, L., & Yang, Q. (2022). Towards personalized federated learning. IEEE Transactions on Neural Networks and Learning Systems. [8] Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018). Federated learning with non-iid data. arXiv preprint arXiv:1806.00582. [9] Jiang, Y., Konečný, J., Rush, K., & Kannan, S. (2019). Improving federated learning personalization via model agnostic meta learning. arXiv preprint arXiv:1909.12488. [10] Zhang, L., Afanasyev, A., Burke, J., Jacobson, V., Claffy, K. C., Crowley, P., ... & Zhang, B. (2014). Named data networking. ACM SIGCOMM Computer Communication Review, 44(3), 66-73. [11] Amadeo, M., Campolo, C., & Molinaro, A. (2016). NDNe: Enhancing named data networking to support cloudification at the edge. IEEE Communications Letters, 20(11), 2264-2267. [12] Amadeo, M., Campolo, C., Iera, A., Molinaro, A., & Ruggeri, G. (2022, May). Client Discovery and Data Exchange in Edge-based Federated Learning via Named Data Networking. In ICC 2022-IEEE International Conference on Communications (pp. 2990-2995). IEEE. [13] Roy, A. G., Siddiqui, S., Pölsterl, S., Navab, N., & Wachinger, C. (2019). Braintorrent: A peer-to-peer environment for decentralized federated learning. arXiv preprint arXiv:1905.06731. [14] Chai, D., Wang, L., Chen, K., & Yang, Q. (2020). Secure federated matrix factorization. IEEE Intelligent Systems, 36(5), 11-20. [15] Mastorakis, S., Afanasyev, A., & Zhang, L. (2017). On the evolution of ndnSIM: An open-source simulator for NDN experimentation. ACM SIGCOMM Computer Communication Review, 47(3), 19-33. [16] Ni, J., Li, J., & McAuley, J. (2019, November). Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP) (pp. 188-197) |
| Description | 碩士 國立政治大學 資訊科學系 110753133 |
| 資料來源 | http://thesis.lib.nccu.edu.tw/record/#G0110753133 |
| Type | thesis |
| dc.contributor.advisor | 蔡子傑 | zh_TW |
| dc.contributor.advisor | Tsai, Tzu-Chieh | en_US |
| dc.contributor.author (Authors) | 蔡明衡 | zh_TW |
| dc.contributor.author (Authors) | Tsai, Ming-Heng | en_US |
| dc.creator (作者) | 蔡明衡 | zh_TW |
| dc.creator (作者) | Tsai, Ming-Heng | en_US |
| dc.date (日期) | 2024 | en_US |
| dc.date.accessioned | 4-Feb-2025 15:43:41 (UTC+8) | - |
| dc.date.available | 4-Feb-2025 15:43:41 (UTC+8) | - |
| dc.date.issued (上傳時間) | 4-Feb-2025 15:43:41 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0110753133 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/155451 | - |
| 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) | 110753133 | zh_TW |
| dc.description.abstract (摘要) | 隨著資訊技術和人工智慧的持續進步，個人智慧設備的發展促使資料分析和 隱私保護的重要性逐漸增加。聯邦學習作為一種新型的機器學習架構，不僅能夠滿足資料隱私的需求，允許分散的資料保持在原始位置，同時還能進行模型的協同訓練。但是聯邦學習在資料非獨立同分佈（Non-IID）情境下，仍面臨諸多挑戰。而個性化聯邦學習是一種有前景的解決方案。本研究聚焦在個性化學習中的客戶端選擇與多中心聯邦學習上面。 本研究以不同網路架構對聯邦學習進行了改良和詳細探討。在 NDN 架構中 進行客戶端選擇可以以更少的網路成本達到較好的效能；而在 P2P 多中心聯邦學習中，雖然需要提高網路成本，但能夠獲得更好的結果。將這兩者結合後，可以同時達到更低的網路成本和較佳的推薦效能。此外，本研究還採用了元學習的概念，在面對推薦系統中新加入的用戶時，取得了良好的結果。 綜上所述，本研究不僅深入探討了個性化聯邦學習所面臨的各種挑戰，還 提出了多種基於網路架構的優化策略，尤其是基於 NDN 網路的客戶端選擇方法，顯著降低聯邦學習的通訊成本並提升推薦效能。這些研究成果對理解和優化聯邦學習具有重要的參考價值，並為未來的研究和實際應用提供了概念性驗證。 | zh_TW |
| dc.description.abstract (摘要) | With the continuous advancement of information technology and artificial intelligence, the development of personal smart devices has emphasized the growing importance of data analysis and privacy protection. Federated learning, as a novel machine learning framework, not only satisfies data privacy requirements by allowing distributed data to remain at their original locations but also enables collaborative model training. However, federated learning faces numerous challenges, particularly under non-independent and identically distributed (Non-IID) data scenarios. Personalized federated learning emerges as a promising solution to address these challenges. This study focuses on client selection and multi-center federated learning within the scope of personalized learning. This research explores and improves federated learning through various network architectures. Client selection within the NDN architecture achieves better performance with reduced network costs, while P2P multi-center federated learning, though incurring higher network costs, delivers superior results. By combining these two approaches, the system achieves lower network costs and improved recommendation performance. Furthermore, the study adopts the concept of meta-learning, achieving favorable results when addressing newly added users in the recommendation system. In conclusion, this study not only delves into the challenges faced by personalized federated learning but also proposes several optimization strategies based on network architectures, particularly the NDN-based client selection method, to significantly reduce communication costs and enhance recommendation performance. These findings provide valuable insights for understanding and optimizing federated learning and offer conceptual validation for future research and practical applications | en_US |
| dc.description.tableofcontents | 摘要 ii Abstract iii 目次 iv 圖目錄 vi 表目錄 viiii 第一章、 緒論 1 第一節、 研究背景與動機 1 第二節、 研究目的 4 第二章、 文獻探討 6 第一節、 聯邦學習 5 第二節、 推薦系統 10 第三節、 個性化聯邦學習 14 第四節、 命名資料網路 16 第三章、 研究方法 20 第一節、 以網路架構特性優化聯邦學習 20 第二節、 聯邦推薦系統 28 第四章、 實驗設計與結果分析 30 第一節、 實驗環境與實驗設計 30 第二節、 實驗評估 33 第三節、 實驗一：資料分佈情形對推薦系統的影響 35 第四節、 實驗二：集中式與分散式推薦系統之比較 36 第五節、 實驗三：轉發次數與推薦項目之客戶端選擇 40 第六節、 實驗四：P2P多中心聯邦推薦系統 43 第七節、 實驗五：NDP2PFL：多中心與客戶端選擇之聯邦推薦系統 46 第八節、 綜合比較 51 第五章、 結論與未來展望 54 參考文獻 55 附錄一 本研究中推薦系統中各類別用戶數量 57 附錄二 聯邦矩陣分解推薦系統的特徵空間選擇 58 附錄三 P2PFL中的節點重複計算問題 59 附錄四 網路模擬細節 60 | zh_TW |
| dc.format.extent | 5981855 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0110753133 | en_US |
| dc.subject (關鍵詞) | 推薦系統 | zh_TW |
| dc.subject (關鍵詞) | 聯邦學習 | zh_TW |
| dc.subject (關鍵詞) | 個性化聯邦學習 | zh_TW |
| dc.subject (關鍵詞) | 命名資料網路 | zh_TW |
| dc.subject (關鍵詞) | 點對點分散式網路 | zh_TW |
| dc.subject (關鍵詞) | 資料隱私 | zh_TW |
| dc.subject (關鍵詞) | Recommender system | en_US |
| dc.subject (關鍵詞) | Federated Learning | en_US |
| dc.subject (關鍵詞) | Personalized Federated Learning | en_US |
| dc.subject (關鍵詞) | Named Data Networking | en_US |
| dc.subject (關鍵詞) | Peer-to-peer | en_US |
| dc.subject (關鍵詞) | Data Privacy | en_US |
| dc.title (題名) | 基於命名式網路架構之個性化聯邦推薦系統 | zh_TW |
| dc.title (題名) | Personalized Federated Recommendation Systems Via Named Data Networking | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | [1] Konečný, J., McMahan, H. B., Ramage, D., & Richtárik, P. (2016). Federated optimization: Distributed machine learning for on-device intelligence. arXiv preprint arXiv:1610.02527. [2] Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 1-19. [3] McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017, April). Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics (pp. 1273-1282). PMLR. [4] Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37. [5] Mohammadi, M. (2020). A new non-negative matrix factorization method to build a recommender system. Journal of Research in Science, Engineering and Technology, 8(2), 6-12. [6] Sun, Z., Xu, Y., Liu, Y., He, W., Kong, L., Wu, F., ... & Cui, L. (2022). A survey on federated recommendation systems. arXiv preprint arXiv:2301.00767. [7] Tan, A. Z., Yu, H., Cui, L., & Yang, Q. (2022). Towards personalized federated learning. IEEE Transactions on Neural Networks and Learning Systems. [8] Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018). Federated learning with non-iid data. arXiv preprint arXiv:1806.00582. [9] Jiang, Y., Konečný, J., Rush, K., & Kannan, S. (2019). Improving federated learning personalization via model agnostic meta learning. arXiv preprint arXiv:1909.12488. [10] Zhang, L., Afanasyev, A., Burke, J., Jacobson, V., Claffy, K. C., Crowley, P., ... & Zhang, B. (2014). Named data networking. ACM SIGCOMM Computer Communication Review, 44(3), 66-73. [11] Amadeo, M., Campolo, C., & Molinaro, A. (2016). NDNe: Enhancing named data networking to support cloudification at the edge. IEEE Communications Letters, 20(11), 2264-2267. [12] Amadeo, M., Campolo, C., Iera, A., Molinaro, A., & Ruggeri, G. (2022, May). Client Discovery and Data Exchange in Edge-based Federated Learning via Named Data Networking. In ICC 2022-IEEE International Conference on Communications (pp. 2990-2995). IEEE. [13] Roy, A. G., Siddiqui, S., Pölsterl, S., Navab, N., & Wachinger, C. (2019). Braintorrent: A peer-to-peer environment for decentralized federated learning. arXiv preprint arXiv:1905.06731. [14] Chai, D., Wang, L., Chen, K., & Yang, Q. (2020). Secure federated matrix factorization. IEEE Intelligent Systems, 36(5), 11-20. [15] Mastorakis, S., Afanasyev, A., & Zhang, L. (2017). On the evolution of ndnSIM: An open-source simulator for NDN experimentation. ACM SIGCOMM Computer Communication Review, 47(3), 19-33. [16] Ni, J., Li, J., & McAuley, J. (2019, November). Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP) (pp. 188-197) | zh_TW |