Читать книгу Fog Computing - Группа авторов - Страница 96

In common practice, DNN models are trained on high-end workstations equipped with powerful GPUs where training data are also located. This is the approach that giant AI companies such as Google, Facebook, and Amazon have adopted. These companies have been collecting a gigantic amount of data from users and use those data to train their DNN models. This approach, however, is privacy-intrusive, especially for mobile phone users because mobile phones may contain the users' privacy-sensitive data. Protecting users' privacy while still obtaining well-trained DNN models becomes a challenge.

To address this challenge, we envision that the opportunity lies in on-device training. As computer resources in edge devices become increasingly powerful, especially with the emergence of AI chipsets, in the near future, it becomes feasible to train a DNN model locally on edge devices. By keeping all the personal data that may contain private information on edge devices, on-device training provides a privacy-preserving mechanism that leverages the compute resources inside edge devices to train DNN models without sending the privacy-sensitive personal data to the giant AI companies. Moreover, today, gigantic amounts of data are generated by edge devices such as mobile phones on a daily basis. These data contain valuable information about users and their personal preferences. With such personal information, on-device training is enabling training personalized DNN models that deliver personalized services to maximally enhance user experiences.