Federated Learning Meets Human Emotions: A Decentralized Framework for Human–Computer Interaction for IoT Applications

摘要

As stated by Spock, "change is the essential process of all existence," which is reflected in everyday applications in our daily lives. We, as humans, just need to find a way to make the best use of the current technological advances. The pandemic has managed to exploit our deepest vulnerabilities and insecurities. We need to cope with a lot of things, just to be comfortable in the new normal. Hence, we can rely on technology, the greatest asset developed by humans. In this article, we discuss how we can enhance the work environment in offices post-pandemic. We combine federated learning with emotion analysis to create a state-of-the-art, simple, secure, and efficient emotion monitoring system. We combine facial expression and speech signals to find out macroexpressions and create an emotion index that is monitored to find the mental health of the user. Federated learning enables users to locally train the model without compromising his/her privacy. In place of sending data to the centralized server, the proposed scheme sends only model weights that are combined at the server to make a better global model, which is further pushed back to the users. This model is then trained interorganizational as it does not violate the privacy or data sharing to achieve optimal results. The data collected from users are monitored to analyze the mental health and presented with counseling solutions during low times. Technology is a panacea that has enabled us to survive in this pandemic, and by using our solution to improve work culture and the environment in post-pandemic times.