Some ways to implement deep learning on mobile devices:
1. Device - Inference Only 僅推論
Pre-trained models by cloud data centers are loaded into device. The model can be either fixed or can be updated later via network or product services.
訓練好的模型放在行動裝置上，模型除了是固定預載的，也可經網路下載或透過維修服務人員更新。
2. Device - Inference and Collect Data for Distributed Training 有回報資訊
The device collects data and transmit them for cloud data centers to train the model, which is then downloaded to update the device.
行動裝置可回報上傳適合更新模型的資訊
3. Federated Learning 聯合學習
Download the model to device and train the model with local data. The results of training and inference are uploaded to the cloud for updating the model.
在行動裝置上同時做訓練及推論/推斷/推理，再將結果上傳雲端，然後還可再下載新的模型至裝置
References:
Wang, J., Cao, B., Yu, P., Sun, L., Bao, W., & Zhu, X. (2018, July). Deep learning towards mobile applications. In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) (pp. 1385-1393). IEEE.
行動裝置上的AI：使用TensorFlow on iOS Android及樹莓派,王眾磊、陳海波, 深智數位, p.3-1~3-3
McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. (2017, April). Communication-efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics (pp. 1273-1282). PMLR.
分布式機器學習時代即將來臨？谷歌推出「Federated Learning」