  Speech Emotion Recognition (SER) refers to the recognition of human emotions
from natural speech. If done accurately, it can offer a number of benefits in
building human-centered context-aware intelligent systems. Existing SER
approaches are largely centralized, without considering users' privacy.
Federated Learning (FL) is a distributed machine learning paradigm dealing with
decentralization of privacy-sensitive personal data. In this paper, we present
a privacy-preserving and data-efficient SER approach by utilizing the concept
of FL. To the best of our knowledge, this is the first federated SER approach,
which utilizes self-training learning in conjunction with federated learning to
exploit both labeled and unlabeled on-device data. Our experimental evaluations
on the IEMOCAP dataset shows that our federated approach can learn
generalizable SER models even under low availability of data labels and highly
non-i.i.d. distributions. We show that our approach with as few as 10% labeled
data, on average, can improve the recognition rate by 8.67% compared to the
fully-supervised federated counterparts.
