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Speech Emotion Recognition (SER) application is frequently associated withprivacy concerns as it often acquires and transmits speech data at theclient-side to remote cloud platforms for further processing. These speech datacan reveal not only speech content and affective information but the speaker'sidentity, demographic traits, and health status. Federated learning (FL) is adistributed machine learning algorithm that coordinates clients to train amodel collaboratively without sharing local data. This algorithm shows enormouspotential for SER applications as sharing raw speech or speech features from auser's device is vulnerable to privacy attacks. However, a major challenge inFL is limited availability of high-quality labeled data samples. In this work,we propose a semi-supervised federated learning framework, Semi-FedSER, thatutilizes both labeled and unlabeled data samples to address the challenge oflimited labeled data samples in FL. We show that our Semi-FedSER can generatedesired SER performance even when the local label rate l=20 using two SERbenchmark datasets: IEMOCAP and MSP-Improv.

Semi-FedSER: Semi-supervised Learning for Speech Emotion Recognition On Federated Learning using Multiview Pseudo-Labeling