Privacy Preserving Gaze Estimation Via Federated Learning Adapted To Egocentric Video

Abstract

This paper presents privacy preserving gaze estimation via federated learning adapted to egocentric videos. Gaze estimation with egocentric video stands out among many applications of wearable cameras and is closely related to many high-tech applications envisioned in the future. However, traditional gaze estimation methods mainly depend on a centralized training model, which has the risk of leakage of private information. In this paper, we propose an innovative transformer-based framework that integrates the principles of federated learning into the gaze estimation process using egocentric video data. By training the model without sharing raw gaze data and only updating parameters, this framework can achieve the dual objectives of enhancing the model’s performance and protecting data privacy. Experimental results demonstrate that our framework not only outperforms other federated learning methods but also achieves performance close to that of methods with data sharing.