Yeah, I’ve been chewing on that. The trick is to compress the model so it runs in‑device without too many queries to the cloud, then use on‑device federated learning or differential privacy to keep the raw data private. The trade‑off is usually a bit of latency for each local inference and some model accuracy loss, but you can tune the quantization and pruning to keep it sweet. Want to dive into a concrete use‑case?