Adversarial Predictions of Data Distributions Across Federated
  Internet-of-Things Devices
Samir Rajani Dario Dematties Nathaniel Hudson Kyle Chard Nicola Ferrier Rajesh Sankaran Peter Beckman
Federated learning (FL) is increasingly becoming the default approach for
training machine learning models across decentralized Internet-of-Things (IoT)
devices. A key advantage of FL is that no raw data are communicated across the
network, providing an immediate layer of privacy. Despite this, recent works
have demonstrated that data reconstruction can be done with the locally trained
model updates which are communicated across the network. However, many of these
works have limitations with regard to how the gradients are computed in
backpropagation. In this work, we demonstrate that the model weights shared in
FL can expose revealing information about the local data distributions of IoT
devices. This leakage could expose sensitive information to malicious actors in
a distributed system. We further discuss results which show that injecting
noise into model weights is ineffective at preventing data leakage without
seriously harming the global model accuracy.