Towards personalized learning in mobile sensing systems

Nowadays, mobile devices have become an important part of our daily life. Numerous mobile sensing applications are enabled by various mobile platforms, which leverage machine learning techniques to detect or classify the events of interest such as human activities and health conditions. To achieve this, each user is required to provide a considerable amount of training samples. However, in practice, a large portion of the users may provide only a few or even zero labels, due to various reasons such as privacy concern or simply laziness. A straightforward solution to this problem is to gather the data of all the users in a central database, and train a global classifier from the combined data. Such global classifier, however, may not work well since it ignores the variety in different users' data. To address this challenge, we propose PLOS, a Personalized Learning framework for mObile Sensing applications. PLOS can jointly model the commonness shared among the users as well as the differences between them, which are inferred from both the label information and the underlying structures of individual data. We further develop the distributed PLOS where the raw data of the users are locally processed so that the users only need to send model parameters to the server. Through extensive experiments on both synthetic data and real mobile sensing systems, we show that the proposed PLOS framework is scalable and efficient in energy, computation, and communication costs, and can achieve more accurate classification results compared with the baseline methods.