Cascading Gradient Inversion via LT-Code Inspired Peeling in Federated Learning
New gradient inversion attacks tied to erasure-coding theory recover 94–100% of ImageNet batches, showing federated learning privacy leakage is underestimated.
The paper connects gradient inversion in federated learning to erasure-correcting code theory, constructing analytic attacks that exceed previously known recovery bounds. The attacks recover batches exactly, with every sample's label, from a single FedSGD round, and certify each recovery without ground-truth data. On eight image and tabular benchmarks, even a passive attacker observing an honestly trained network recovers 94–100% of ImageNet batches up to size 128, and more than 90% actively at batch sizes of several hundred. The authors conclude that federated learning's privacy leakage has been underestimated.