A PRIVACY-PRESERVING FEDERATED MULTIMODAL EMOTIONAL INTELLIGENCE FRAMEWORK FOR ADAPTIVE HUMAN–ROBOT COLLABORATION IN INDUSTRY 5.0

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

The fast development of Industry 5.0 highlights the need for human-centric automation that requires the cooperation of intelligent robots with humans based on their emotional, cognitive, and behavioral states. The conventional approaches to Human-Robot Collaboration (HRC) include perception and decision making related to tasks but not the ability of robots to perceive dynamic human states. The contribution of this paper is developing the Federated Multimodal Emotional Intelligence Framework (FMEI) that is required for adaptive HRC based on privacy-preserving federated learning and multimodal affective computing. In particular, the framework involves multimodal fusion to process heterogeneous emotional cues including facial expression, speech signal, physiological, and behavioral modalities. The distributed federated learning algorithm allows training the models in collaboration between robots and edge devices without sending private human data to central servers. The developed model will recognize emotions, predict human states, and generate adaptive robot actions. The goal of the framework is increasing the safety, trust, efficiency, and personalization of collaboration in industrial environment. Experimental evaluation may involve multimodal emotion datasets and simulated industrial robotic applications. Some performance criteria to measure may include accuracy, F1-score, latency, communication efficiency, and privacy preservation.

References

1. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. Agüera y Arcas,
2. “Communication-Efficient Learning of Deep Networks from Decentralized Data,” Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, USA, pp. 1273–1282, 2017.
3. P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, et al.,
4. “Advances and Open Problems in Federated Learning,” Foundations and Trends® in Machine Learning, vol. 14, no. 1–2, pp. 1–210, 2021.
5. Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated Machine Learning: Concept and Applications,” ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 2, pp. 1–19, 2019.
6. T. Li, A. K. Sahu, A. Talwalkar, and V. Smith,“Federated Learning: Challenges, Methods, and Future Directions,” IEEE Signal Processing Magazine, vol. 37, no. 3, pp. 50–60, 2020.
7. R. Yang, Y. Xu, and X. Chen,"Privacy-Preserving Federated Learning for Intelligent Industrial Systems: A Survey," IEEE Transactions on Industrial Informatics, vol. 18

 

Keywords

Federated Learning, Multimodal AI, Emotional Intelligence, Human–Robot Collaboration, Affective Computing, Industry 5.0, Deep Learning, Privacy-Preserving AI.