A bit-flipping mechanism for wireless federated learning claims Rényi differential privacy from channel noise, but the proof uses an expected bit-level distance rather than a worst-case sensitivity, leaving the guarantee unproven.
Pile: Robust privacy-preserving federated learning via verifiable perturbations,
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Free Privacy Protection for Wireless Federated Learning: Enjoy It or Suffer from It?
A bit-flipping mechanism for wireless federated learning claims Rényi differential privacy from channel noise, but the proof uses an expected bit-level distance rather than a worst-case sensitivity, leaving the guarantee unproven.