  Distributed learning such as federated learning or collaborative learning
enables model training on decentralized data from users and only collects local
gradients, where data is processed close to its sources for data privacy. The
nature of not centralizing the training data addresses the privacy issue of
privacy-sensitive data. Recent studies show that a third party can reconstruct
the true training data in the distributed machine learning system through the
publicly-shared gradients. However, existing reconstruction attack frameworks
lack generalizability on different Deep Neural Network (DNN) architectures and
different weight distribution initialization, and can only succeed in the early
training phase. To address these limitations, in this paper, we propose a more
general privacy attack from gradient, SAPAG, which uses a Gaussian kernel based
of gradient difference as a distance measure. Our experiments demonstrate that
SAPAG can construct the training data on different DNNs with different weight
initializations and on DNNs in any training phases.
