Federated Learning (FL) has emerged as a transformative approach for training machine learning models across distributed devices without transferring raw user data, making it particularly suitable for on-device personal assistants. This paper investigates the performance, privacy, and security trade-offs inherent in applying FL to personal assistant systems. It explores how FL enables personalized experiences while preserving user confidentiality, and evaluates challenges related to model accuracy, communication overhead, adversarial attacks, and data heterogeneity. Through a critical review of recent advancements and experimental frameworks, the study outlines design considerations for optimizing FL deployments in real-world personal assistant applications. The paper concludes by proposing future research directions to enhance the robustness, efficiency, and trustworthiness of FL-powered personal assistants.

Federated Learning, On-Device Personal Assistants, Privacy-Preserving AI

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