  Spatiotemporal federated learning has recently raised intensive studies due
to its ability to train valuable models with only shared gradients in various
location-based services. On the other hand, recent studies have shown that
shared gradients may be subject to gradient inversion attacks (GIA) on images
or texts. However, so far there has not been any systematic study of the
gradient inversion attacks in spatiotemporal federated learning. In this paper,
we explore the gradient attack problem in spatiotemporal federated learning
from attack and defense perspectives. To understand privacy risks in
spatiotemporal federated learning, we first propose Spatiotemporal Gradient
Inversion Attack (ST-GIA), a gradient attack algorithm tailored to
spatiotemporal data that successfully reconstructs the original location from
gradients. Furthermore, we design an adaptive defense strategy to mitigate
gradient inversion attacks in spatiotemporal federated learning. By dynamically
adjusting the perturbation levels, we can offer tailored protection for varying
rounds of training data, thereby achieving a better trade-off between privacy
and utility than current state-of-the-art methods. Through intensive
experimental analysis on three real-world datasets, we reveal that the proposed
defense strategy can well preserve the utility of spatiotemporal federated
learning with effective security protection.
