Federated Multitask Learning for Pedestrian Location-Aware 5G Multicast/Broadcast Services

5G multicast/broadcast services can provide transformative new opportunities as mobile devices proliferate. However, realizing the full potential of these services requires real-time pedestrian localization. We propose a federated multitask learning (FML) approach on smartphones to enable pedestrian location-aware 5G multicast/broadcast services. Our lightweight FML architecture provides accurate real-time localization while preserving privacy. The pedestrian location data enables adaptive 5G network planning, contextual location-based services, quality of service improvements, and load balancing. Simulations demonstrate the effectiveness of our FML scheme for accurate pedestrian localization. They also highlight significant enhancements to 5G multicast/broadcast services enabled by real-time pedestrian positioning. In summary, our work facilitates enhanced 5G multicast/broadcast services through federated on-device learning for real-time pedestrian localization.