Personalized Federated Learning for Egocentric Video Gaze Estimation with Comprehensive Parameter Frezzing

TL;DR

This paper introduces FedCPF, a personalized federated learning method for egocentric video gaze estimation that selectively freezes significant parameters to improve personalization and accuracy across diverse user data.

Contribution

The paper proposes a novel parameter freezing strategy based on parameter significance, integrated into a federated learning framework for egocentric gaze estimation.

Findings

FedCPF outperforms existing federated learning methods in accuracy metrics.

Selective parameter freezing enhances model personalization.

Extensive experiments validate the effectiveness of the approach.

Abstract

Egocentric video gaze estimation requires models to capture individual gaze patterns while adapting to diverse user data. Our approach leverages a transformer-based architecture, integrating it into a PFL framework where only the most significant parameters, those exhibiting the highest rate of change during training, are selected and frozen for personalization in client models. Through extensive experimentation on the EGTEA Gaze+ and Ego4D datasets, we demonstrate that FedCPF significantly outperforms previously reported federated learning methods, achieving superior recall, precision, and F1-score. These results confirm the effectiveness of our comprehensive parameters freezing strategy in enhancing model personalization, making FedCPF a promising approach for tasks requiring both adaptability and accuracy in federated learning settings.