Federated Learning (FL) has increasingly been promoted as a practical way to train models over distributed edge data while avoiding raw-data centralization, yet most published evaluations have remained confined to a single task and therefore have provided limited evidence about how FL behaves across substantially different workloads. In this paper, a multi-domain empirical study of FL is presented in two representative edge settings: vision-based object detection for connected vehicles and time-series energy forecasting for smart-grid assets.