  Enhancing the energy efficiency of buildings significantly relies on
monitoring indoor ambient temperature. The potential limitations of
conventional temperature measurement techniques, together with the omnipresence
of smartphones, have redirected researchers'attention towards the exploration
of phone-based ambient temperature estimation methods. However, existing
phone-based methods face challenges such as insufficient privacy protection,
difficulty in adapting models to various phones, and hurdles in obtaining
enough labeled training data. In this study, we propose a distributed
phone-based ambient temperature estimation system which enables collaboration
among multiple phones to accurately measure the ambient temperature in
different areas of an indoor space. This system also provides an efficient,
cost-effective approach with a few-shot meta-learning module and an automated
label generation module. It shows that with just 5 new training data points,
the temperature estimation model can adapt to a new phone and reach a good
performance. Moreover, the system uses crowdsourcing to generate accurate
labels for all newly collected training data, significantly reducing costs.
Additionally, we highlight the potential of incorporating federated learning
into our system to enhance privacy protection. We believe this study can
advance the practical application of phone-based ambient temperature
measurement, facilitating energy-saving efforts in buildings.
