A Review on On Device Privacy and Machine Learning Training
Paul Tonga, Zubaida Said Ameen, Auwalu Saleh Mubarak, Fadi M. Al-Turjman · 2022 International Conference on Artificial Intelligence in Everything (AIE) · 2022
On device machine learning has in recent times attracted growing attention and applications, ranging from advanced OCR and handwriting recognition, smart sleep tracking in Apple iOS, mobile virtual assistants and Image recognition, text translation in android; and more. As a result, there is increasing demand for user privacy and security in machine learning applications. Federated Learning (FL), a new method, allows users to cooperatively train a global model while keeping the data local, thereby resolving the issue of data privacy protection through the encryption mechanism. The server aggregates models until convergence while the clients train their local models. In this procedure, the server employs a reward system to persuade users to provide huge amounts of high-quality data in order to enhance the overall model. This paper explores a few FL techniques that can be applied alongside on-device learning, in order to achieve better privacy and security.