@inproceedings{
dimitrov2024spear,
title={{SPEAR}: Exact Gradient Inversion of Batches in Federated Learning},
author={Dimitar Iliev Dimitrov and Maximilian Baader and Mark Niklas Mueller and Martin Vechev},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lPDxPVS6ix}
}
Federated learning is a framework for collaborative machine learning where clients only share gradient updates and not their private data with a server. However, it was recently shown that gradient inversion attacks can reconstruct this data from the shared gradients. In the important honest-but-curious setting, existing attacks enable exact reconstruction only for batch size of $b=1$, with larger batches permitting only approximate reconstruction. In this work, we propose SPEAR, *the first algorithm reconstructing whole batches with $b >1$ exactly*. SPEAR combines insights into the explicit low-rank structure of gradients with a sampling-based algorithm. Crucially, we leverage ReLU-induced gradient sparsity to precisely filter out large numbers of incorrect samples, making a final reconstruction step tractable. We provide an efficient GPU implementation for fully connected networks and show that it recovers high-dimensional ImageNet inputs in batches of up to $b \lesssim 25$ exactly while scaling to large networks. Finally, we show theoretically that much larger batches can be reconstructed with high probability given exponential time.
SPEAR: Exact Gradient Inversion of Batches in Federated Learning
Dimitar Iliev Dimitrov Maximilian Baader Mark Niklas Mueller Martin Vechev