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Machine learning is increasingly used to produce predictive models for crowdsensing applications such as health monitoring and query suggestion. These models are more accurate when trained on large amount of data collected from different sources. However, such massive data collection presents serious privacy concerns. The personal crowdsensing data such as photos, voice records, and locations is often highly sensitive, and once being sent out to the collecting companies, falls out of the control of the crowdsensing users who own it. This may preclude the practice of transmitting all user data to a central location and training there using conventional machine learning approaches. In this paper, we advocate an alternative approach that leaves data stored on the user side and learns a shared model by coordinating local training of crowdsensing users in an iterative process. Specifically, we focus on …