This is different from differential privacy (which, btw, isn't just an apple thing). Differential privacy essentially says some responses will be lies, but that

This is different from differential privacy (which, btw, isn't just an apple thing). Differential privacy essentially says some responses will be lies, but that we can still get truthful aggregate information. The canonical example is the following process: Flip a coin, if it's head, tell me whether you're a communist. If it's tails, flip another coin and if that one comes up heads, tell me you're a communist, and if it's tails, tell me you're not. From one run, you can't tell if any individual is telling the truth, but you can still estimate the number of communists from the aggregate responses. This is doing local model training, and sending the model updates, instead of the raw data that would usually be used for training.