本課程目標在於訓練同學快速開發物聯網智慧應用，以聯邦學習進行閘道器分散式訓練，配合kubeflow管線式開發，練習建置聯邦學習容器，導入kubeflow自動部署，進行聯邦模型參數自動更新(CI/CD)，最後結合Node red 圖形式開發kubeflow 聯邦學習管線，供工程師快速開發物聯網智慧應用，並以模板技術(template)結合容器式開發，實作各種聯邦學習，包含縱向、橫向及聯邦遷移，最後貢獻於Node-red及kubeflow開源社群。

The goal of this course is to train students to quickly develop smart applications for the Internet of Things, use federated learning to conduct distributed training of gateways, cooperate with kubeflow pipeline development, practice building federated learning containers, import kubeflow for automatic deployment, automatically update federated model parameters (CI/CD), and finally develop kubeflow in combination with Node red graphics The federated learning pipeline allows engineers to quickly develop smart applications for the Internet of Things, and uses template technology (template) combined with container development to implement various federated learning, including vertical, horizontal and federated migration, and finally contributes to the Node-red and kubeflow open source communities.

1. Maneesh Rao, Internet of Things with Raspberry Pi 3, Apr. 2018.
2. 本計畫課程模組B-1，家電與能源管理應用開發模組教材
3. 本計畫課程模組B-4 ，深度學習時空間資料探勘教材
4. 本計畫課程模組B-3，家電與能源管理應用開發模組教材
5. 其他參考論文或網路上資料之自編教材

1. Maneesh Rao, Internet of Things with Raspberry Pi 3, Apr. 2018.
2. This project course module B-1, home appliances and energy management application development module teaching materials
3. This project course module B-4, deep learning time-spatial data exploration teaching material
4. This project course module B-3, home appliances and energy management application development module teaching materials
5. Self-edited textbooks based on other reference papers or information on the Internet