A systematic survey on the application of federated learning in mental state detection and human activity recognition

Abstract

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This systematic review investigates the application of federated learning in mental health and human activity recognition. A comprehensive search was conducted to identify studies utilizing federated learning for these domains. The included studies were evaluated based on publication year, task, dataset characteristics, federated learning algorithms, and personalization methods. The aim is to provide an overview of the current state-of-the-art, identify research gaps, and inform future research directions in this emerging field.

Keywords

• mental health
• well-being
• human activity detection
• federated learning
• data heterogeneity
• personalization