Thanks so much for the feedback. I agree with you, regarding privacy it’s much more a personal preference 
. Other decisions are based on what’s possible under iOS and acceptable for the Apple app review process. Again, I feel that the approach I am presenting is much more of an addon and not a replacement. The biggest benefit, is without doubt, for iOS users, as solutions as esp32-mqtt-rooms are simply not available there.
The latency argument is probably also true for most cases, though with an increasing number of ESP32s it will get noticeable (e.g. due to the shared BT/Wifi hardware on the ESP32, CSMA/CA medium access). But yes, probably not an issue.
Yes, also a good point. I like to train my models using sklearn and/or Keras. The current implementation allows importing a new model with a single button from a URL 
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Definitely a good point, and one of the biggest drawbacks of the device-centric approach.
From what I understand, all approaches do some sort of AI-based classification using RSSI values. I do not expect significant improvements/drawbacks among them. RSSI is not a real high-value wireless channel feature, just the best we can get with cheap hardware and reasonable effort at the moment.
A real game-changer would be to use significantly more expensive SDR hardware and extract more advanced wireless channel features for the learning, i.e. multipath-parameters, or time-difference of arrival.
I hope this is not taking it to far off-topic, but I really appreciate the input and wanted to explain my thought-process as thoroughly as possible.