Differential privacy is useful for training or updating a public model where individuals' features should be kept private. In floc's case the model is public b

Differential privacy is useful for training or updating a public model where individuals' features should be kept private. In floc's case the model is public but isn't being trained on individual's features in realtime, only used for inference as far as the proposal says, e.g. the proof of concept stage will develop a fixed model that all browser instances (of a given vendor) share. Individuals' features are kept private to the extent that the model output can't be effectively reverse-engineered. Differential privacy probably also won't be useful in the POC stage because the training will require accurate labels which defeats privacy.