| Full text | |
| Author(s): |
Borges, Guilherme Antonio
;
Santos dos Anjos, Julio Cesar
;
Silva, Jorge Sa
Total Authors: 3
|
| Document type: | Journal article |
| Source: | 2024 IEEE INTERNATIONAL CONFERENCE ON SMART COMPUTING, SMARTCOMP 2024; v. N/A, p. 6-pg., 2024-01-01. |
| Abstract | |
Human-in-the-loop cyber-physical Systems use data from various sources to provide valuable assistance to users, but privacy concerns arise when sensitive information is shared. Federated Learning is a promising solution that enables the processing of user data without sharing sensitive information. While this method holds great potential, its efficacy in detecting sleep problems remains an open question. In this way, using a real-world dataset application, our study meticulously evaluates and comprehends the impact of incorporating Federated Learning on sleep detection. Our study evaluates the impact of incorporating Federated Learning on sleep detection and compares it with traditional Machine Learning models. Our findings reveal that our approach delivers accurate sleep detection results over 84% on par with conventional techniques. Our results emphasize the critical importance of handling human error inputs, as this factor significantly influences the accuracy of results in both methods. (AU) | |
| FAPESP's process: | 20/09706-7 - CEREIA - Reference Center on Artificial Intelligence |
| Grantee: | José Soares de Andrade Júnior |
| Support Opportunities: | Research Grants - Research Centers in Engineering Program |