Well, forget Apple for a moment (that was just an example, since parent asked about them specifically): my point was what Google's describing is separate from d

Well, forget Apple for a moment (that was just an example, since parent asked about them specifically): my point was what Google's describing is separate from differential privacy. There's no controlled noise or randomness being applied. They even say at the end of the paper: "While federated learning offers many practical privacy benefits, providing stronger guarantees via differential privacy, secure multi-party computation, or their combination is an interesting direction for future work." So, the "practical privacy benefits" here is referring to the dimensionality reduction from running the raw data thru the LSTM.