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gradient-inversion

topic1 events
papersSEP 10 04:00 UTC

Cascading Gradient Inversion via LT-Code Inspired Peeling in Federated Learning

A new arXiv paper introduces a gradient inversion attack that reconstructs clients' training data from the model updates shared in federated learning. Inspired by LT fountain codes, the method cascades analytic reconstructions through a peeling-style decoding, extending such attacks to larger batches where earlier closed-form inversion approaches break down. The findings underscore that sharing gradients rather than raw data does not fully protect client privacy.

privacy/federated-learning/security/attacks