(Full disclosure: I work at Fritz, and we publish Heartbeat).

There are a couple of legitimate reasons that the on-device model is lower accuracy: compute and size. Models are often reduced in complexity when running on edge devices, but cloud-based GPU (TPU in Google's case) models can require far more calculations in order to squeeze that bit of accuracy. As for model size, a 100MB model in the cloud is not a big deal, but having to download a 100MB model on every edge device is expensive in both time and bandwidth.

Outside of these reasons, Google/Firebase may want on-device capabilities limited in order push adoption of their cloud ML services.