  Mobile Crowdsensing has become main stream paradigm for researchers to
collect behavioral data from citizens in large scales. This valuable data can
be leveraged to create centralized repositories that can be used to train
advanced Artificial Intelligent (AI) models for various services that benefit
society in all aspects. Although decades of research has explored the viability
of Mobile Crowdsensing in terms of incentives and many attempts have been made
to reduce the participation barriers, the overshadowing privacy concerns
regarding sharing personal data still remain. Recently a new pathway has
emerged to enable to shift MCS paradigm towards a more privacy-preserving
collaborative learning, namely Federated Learning. In this paper, we posit a
first of its kind framework for this emerging paradigm. We demonstrate the
functionalities of our framework through a case study of diversifying two
vision algorithms through to learn the representation of ordinary sidewalk
obstacles as part of enhancing visually impaired navigation.
