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| 研究生： |
白力尹 Bai, Li-Yin |
|---|---|
| 論文名稱： |
基於安全的混合縱向和橫向聯邦學習框架適用於二元分類應用 A Secure Vertical and Horizontal Federated Learning Framework for Binary Classification Application |
| 指導教授： |
蔡佩璇
Tsai, Pei-Hsuan |
| 學位類別： |
碩士 Master |
| 系所名稱： |
電機資訊學院 - 製造資訊與系統研究所 Institute of Manufacturing Information and Systems |
| 論文出版年： | 2023 |
| 畢業學年度： | 111 |
| 語文別： | 中文 |
| 論文頁數： | 20 |
| 中文關鍵詞： | 縱向聯邦學習 、橫向聯邦學習 、同態加密 、縱向和橫向聯邦學習 |
| 外文關鍵詞： | Vertical Federated Learning, Horizontal Federated Learning, Homomorphic Encryption, Vertical-Horizontal Federated Learning |
| 相關次數： | 點閱：86 下載：0 |
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為了確保不同機構之間能夠保護資料安全並且進行模型訓練，本篇論文中結合了縱向聯邦學習以及橫向聯邦學習，並且使用同態加密對特徵資料和權重加密的方式，設計一個安全的混合縱向聯邦學習和橫向聯邦學習的框架，適用於二元分類應用。用以實現相同領域擁有相同特徵資料的各個機構與不同領域擁有相同客戶樣本的機構，能夠以資料不公開的方式安全的進行聯邦學習的訓練。
To ensure data security and model training among different institutions, this paper combines vertical federated learning and horizontal federated learning. It utilizes homomorphic encryption to encrypt feature data and model weights, designing a secure framework for hybrid vertical and horizontal federated learning. This framework is suitable for binary classification applications. It enables various institutions within the same field, possessing the same feature data, and institutions from different field with identical client samples, to securely conduct federated learning training without exposing their data.
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[2] HARDY, Stephen, et al. Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. arXiv preprint arXiv:1711.10677, 2017.
[3] MCMAHAN, Brendan, et al. Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics. PMLR, 2017. p. 1273-1282.
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校內：2028-08-24公開校外：2028-08-24公開