Securewear: Federated Learning-Driven Pendant for Women’s Protection

Abstract: Women’s safety remains a critical global issue, necessitating innovative solutions that offer real-time protection while ensuring privacy. This paper proposes a Smart Pendant that leverages Federated Learning to enhance security through decentralized, privacy-preserving intelligence. The device integrates biometric sensors, motion detection, and audio analysis to detect distress situations and automatically trigger emergency alerts without requiring manual intervention. Unlike traditional safety solutions that rely on centralized data processing, federated learning enables local model training, ensuring data security and personalized threat detection while continuously improving performance.

The proposed system incorporates GPS tracking, real-time communication, and AI-driven threat assessment, allowing seamless interaction with emergency contacts and law enforcement. By utilizing federated learning, the smart pendant adapts to diverse user environments and behaviors, enhancing its ability to detect potential threats more accurately over time. This paper explores the technical framework, implementation challenges, and benefits of the proposed system, demonstrating how wearable technology powered by federated learning can significantly improve women’s safety.

Smart Wearable, Federated Learning (FL), Anomaly Detection, Wearable Technology

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