Survey of Federated Learning Models for Spatial-Temporal Mobility Applications

Spatial temporal mobility data collected by location-based services (LBS) [42] and other means such as Call Data Records (CDR), WiFi hotspots, smart watches, cars, etc. is very useful from a socio-economical perspective as it is at the heart of many useful applications (e.g., navigation, geo-located search, geo-located games) and it allows answering numerous societal research questions [51]. For example, Call Data Records have been successfully used to provide real-time traffic anomaly as well as event detection [90, 92], and a variety of mobility datasets have been used in shaping policies for urban communities [31] or epidemic management in the public health domain [80, 79]. From an individual-level perspective, users can benefit from personalized recommendations when they are encouraged to share their location data with third parties [22]. While there is no doubt about the usefulness of location-based applications, privacy concerns regarding the collection and sharing of individuals' mobility traces or aggregated flow of movements have prevented the data from being utilized to their full potential [87, 9, 53]. Indeed, various studies have shown that numerous threats are open if location data falls into the hands of inappropriate parties. These threats include re-identification [68], the inference of sensitive information about users [53, 94](e.g., their home and work locations, religious beliefs, political interests or sexual