  Gaze estimation methods have significantly matured in recent years, but the
large number of eye images required to train deep learning models poses
significant privacy risks. In addition, the heterogeneous data distribution
across different users can significantly hinder the training process. In this
work, we propose the first federated learning approach for gaze estimation to
preserve the privacy of gaze data. We further employ pseudo-gradient
optimisation to adapt our federated learning approach to the divergent model
updates to address the heterogeneous nature of in-the-wild gaze data in
collaborative setups. We evaluate our approach on a real-world dataset
(MPIIGaze) and show that our work enhances the privacy guarantees of
conventional appearance-based gaze estimation methods, handles the convergence
issues of gaze estimators, and significantly outperforms vanilla federated
learning by 15.8% (from a mean error of 10.63 degrees to 8.95 degrees). As
such, our work paves the way to develop privacy-aware collaborative learning
setups for gaze estimation while maintaining the model's performance.
