A systematic survey on the application of federated learning in mental state detection and human activity recognition
Albin Grataloup, Mascha Kurpicz-Briki · 2024
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Description
Grataloup and Kurpicz-Briki surveyed federated learning for mental state detection and human activity recognition in twenty twenty-four. Federated learning trains models across many devices without gathering raw data in one place. That privacy-friendly design suits health data from phones and wearables.
The survey gathered studies using this approach in the two domains. Each study was charted by year, task, dataset traits, the federated algorithm used, and how models were personalized per user. The authors aimed to map the current state of the art. They also sought gaps to guide future work. Their overview frames a young field where mental health sensing and activity tracking meet privacy-preserving machine learning.
Catalog record
| CREATOR | Albin Grataloup, Mascha Kurpicz-Briki |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 61 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |