SPEAR:Exact Gradient Inversion of Batches in Federated Learning

arXiv:2403.03945v1 Announce Type: cross
Abstract: Federated learning is a popular framework for collaborative machine learning where multiple clients only share gradient updates on their local data with the server and not the actual data. Unfortunately, it was recently shown that gradient inversion attacks can reconstruct this data from these shared gradients. Existing attacks enable exact reconstruction only for a batch size of $b=1$ in the important honest-but-curious setting, with larger batches permitting only approximate reconstruction. In this work, we propose …

arxiv attacks can clients cs.cr cs.dc cs.lg data federated federated learning framework local machine machine learning popular server share updates