Federated learning in Emotion Recognition Systems based on physiological signals for privacy preservation: a review

Automated Emotion Recognition Systems (ERS) with physiological signals help improve health and decision-making in everyday life. It uses traditional Machine Learning (ML) methods, requiring high-quality learning models for physiological data (sensitive information). However, automated ERS enables data attacks and leaks, significantly losing user privacy and integrity. This privacy problem can be solved using a novel Federated Learning (FL) approach, which enables distributed machine learning model training. This review examines 192 papers focusing on emotion recognition via physiological signals and FL. It is the first review article concerning the privacy of sensitive physiological data for an ERS. The paper reviews the different emotions, benchmark datasets, machine learning, and federated learning approaches for classifying emotions. It proposes a novel multi-modal Federated Learning for Physiological signals based on Emotion Recognition Systems (Fed-PhyERS) architecture, experimenting with the AMIGOS dataset and its applications for a next-generation automated ERS. Based on critical analysis, this paper provides the key takeaways, identifies the limitations, and proposes future research directions to address gaps in previous studies. Moreover, it reviews ethical considerations related to implementing the proposed architecture. This review paper aims to provide readers with a comprehensive insight into the current trends, architectures, and techniques utilized within the field.