INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue III, March 2025
www.ijltemas.in Page 421
Securewear: Federated Learning-Driven Pendant for Women’s
Protection
P Krishnamoorthy
Associate Professor, Department of Computer Science and Engineering, Sasi Institute of Technology & Engineering,
Tadepalligudem
DOI : https://doi.org/10.51583/IJLTEMAS.2025.140300047
Received: 31 March 2025; Accepted: 04 April 2025; Published: 17 April 2025
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.
Keywords: Smart Wearable, Federated Learning (FL), Anomaly Detection, Wearable Technology
I. Introduction
Women’s safety remains a pressing concern in today’s world, where incidents of harassment and violence continue to rise.
Traditional safety measures, such as mobile applications and emergency helplines, often require manual intervention, which may
not always be feasible in critical situations. To address this challenge, smart wearable technology has emerged as a promising
solution, offering real-time monitoring and automated emergency response.
This paper proposes a Smart Pendant that leverages Federated Learning (FL) to enhance safety and security. Unlike conventional
wearables that rely on centralized data processing, FL enables decentralized, privacy-preserving intelligence by training machine
learning models locally on the device. This approach ensures user data remains secure while improving the system’s ability to
detect and respond to threats based on real-world scenarios.
The smart pendant is designed with multiple safety features, including real-time distress detection, automatic emergency alerts,
and location tracking. It integrates biometric sensors, motion detection, and audio analysis to assess potential danger and
autonomously trigger alerts when necessary. By incorporating FL, the device continuously learns from user interactions,
improving accuracy without compromising privacy.
This paper explores the technical design, implementation, and advantages of using FL in wearable safety devices, highlighting its
potential to revolutionize personal security solutions for women.
II. Smart Wearable’s
Smart wearable’s have revolutionized personal security, especially for women, by integrating Artificial Intelligence (AI), Internet
of Things (IoT), and Federated Learning (FL) to provide real-time protection. Among these innovations, the smart necklace
stands out as a discreet yet powerful safety device. Unlike traditional wearable’s, a smart necklace combines biometric sensors,
GPS tracking, and emergency alert systems into a compact and stylish accessory that seamlessly blends with daily wear.
By leveraging machine learning (ML) and gesture recognition, a smart necklace can detect anomalous movements, voice distress
signals, or sudden impacts, automatically triggering alerts to emergency contacts. Edge computing ensures quick response times,
while privacy-preserving AI techniques like secure data aggregation and differential privacy help protect user information. Some
advanced models also feature NLP for emergency detection, enabling voice-activated distress signals.
With real-time threat detection, IoT connectivity, and wearable technology advancements, the smart necklace is transforming into
a reliable safety companion, offering a proactive approach to women’s security while maintaining elegance and convenience.
Future developments aim to enhance battery life, AI accuracy, and network independence for even greater reliability.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue III, March 2025
www.ijltemas.in Page 422
Wearable Technology
Wearable technology refers to smart electronic devices designed to be worn on the body, integrating sensors, artificial intelligence
(AI), and connectivity features to enhance convenience, health monitoring, and security. These devices have evolved from simple
fitness trackers to advanced AI-powered wearables used in various fields, including healthcare, sports, and personal safety.
One emerging innovation in wearable technology is the smart necklace, designed specifically for women’s safety. Unlike
conventional wearables, a smart necklace discreetly integrates biometric sensors, GPS tracking, IoT connectivity, and real-time
emergency alert systems. With features such as gesture recognition, voice distress detection, and AI-powered anomaly detection,
these devices can proactively assess threats and send alerts to emergency contacts or law enforcement.
Wearable technology continues to advance with the incorporation of Federated Learning (FL) and Edge Computing, ensuring
privacy-preserving AI while reducing reliance on cloud-based processing. Future developments aim to improve battery
efficiency, miniaturization, and real-time data analytics, making wearable technology more intelligent, responsive, and essential
for personal security, health tracking, and everyday convenience.
Federated Learning
With the increasing adoption of smart wearable devices, there is a growing need for privacy-preserving AI solutions that process
sensitive data without compromising security. Federated Learning (FL) has emerged as a key technology that allows AI models to
be trained across multiple devices without sharing raw data. This makes it highly relevant for applications such as smart
necklaces, which can detect distress signals, track location, and trigger emergency alerts while ensuring user privacy.
FL enables devices to train local AI models independently and send only model updates to a central server for aggregation.
Unlike traditional machine learning, where all data is stored and processed centrally, FL offers:
Decentralized Training – Data remains on the user’s device.
Enhanced Privacy – No raw data transmission, reducing security risks.
Personalization – AI models adapt to individual user patterns.
Scalability – Multiple devices contribute to improving global models.
In wearable technology, FL is increasingly being used for health monitoring, activity recognition, and security applications.
III. Research Methodology
System Design
The proposed smart pendant system is designed to enhance women's safety through real-time distress detection and response
mechanisms. The system architecture includes biometric sensors, motion detection, and an AI-based anomaly detection model
utilizing FL. The key components of the system include:
Biometric Sensors: Used for heart rate monitoring and stress level detection.
Motion Detection: Captures sudden movements, falls, or struggles.
Microphone & Audio Analysis: Identifies distress calls or specific voice commands.
GPS & Connectivity Modules: Enables real-time location tracking and communication with emergency contacts.
Federated Learning-based AI Model: Ensures privacy-preserving intelligence, continuously learning from user
interactions.
Federated Learning Approach
Unlike traditional cloud-based machine learning models, FL allows the smart pendant to train locally on user devices while
aggregating model updates on a central server. The methodology follows these steps:
1. Local Training: Each device trains its AI model using personal sensor data.
2. Secure Model Update Transmission: Only model updates (not raw data) are sent to the central server.
3. Global Model Aggregation: The central server combines updates to improve accuracy.
4. Model Deployment: The enhanced model is sent back to all devices, improving security detection across users.
This approach ensures privacy, reduces bandwidth consumption, and enables personalized threat detection without exposing
sensitive user data.
Data Collection and Preprocessing
Data used for training the AI model includes:
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue III, March 2025
www.ijltemas.in Page 423
Physiological Data: Heart rate, stress levels, and motion patterns.
Audio Samples: Voice commands and distress signals.
GPS Data: Location history and movement patterns.
Preprocessing steps include noise reduction, feature extraction, and labeling of distress situations. The data is then used to train
AI models for real-time safety assessment.
Evaluation Metrics
To assess the effectiveness of the smart pendant, we use the following performance metrics:
Accuracy: Measures the correct classification of distress and normal situations.
False Positive Rate: Ensures minimal false alarms.
Latency: Evaluates real-time response speed.
Battery Efficiency: Measures power consumption of the wearable device.
The proposed methodology ensures an efficient, privacy-preserving, and adaptive safety solution for women.
IV. Related Works
Author(s)
& Year
Title
Objective/Focus
Method/Algorithm
Used
Key Findings
Limitations/Futu
re Directions
Singh et al.
(2023)
AI-Powered
Edge
Computing for
Wearables
Reduce latency in
AI wearables
through edge
computing
Edge AI, Deep
Learning, IoT
Faster response times
and lower energy
consumption
AI model requires
real-world testing
for optimization
Brown et al.
(2023)
Wearable IoT
for Women’s
Safety
Use IoT and cloud
integration for
safety devices
IoT, Cloud Storage,
GPS tracking
Provided efficient
location-based alerts
Dependent on
stable network for
real-time tracking
Park & Kim
(2023)
FL-Enhanced
Smart
Wearables
Optimize AI models
in wearables using
FL
Federated Learning,
Local Model
Training
Enhanced
personalization and
security
FL requires more
computational
power
Li et al.
(2022)
Privacy-
Preserving AI
in Wearables
Enhance privacy in
AI-based wearables
using federated
learning
Federated Learning
(FL), Differential
Privacy
FL improved privacy
and model
performance without
sharing raw data
Increased device
computational
requirements
Kumar &
Das (2022)
Real-Time
Threat
Assessment in
Wearables
Develop a real-time
AI system for
detecting threats
AI-based Motion
Recognition,
Biometric Sensors
Device recognized
sudden movements
indicating distress
Limited dataset,
needs large-scale
training
Ahmed &
Malik
(2022)
AI-Driven
Safety Devices
for Women
Improve real-time
detection of safety
threats
AI, NLP for Voice
Commands, Gesture
Recognition
AI recognized distress
gestures with 90%
accuracy
Energy
consumption and
battery life
limitations
Kim & Lee
(2021)
IoT-Based
Smart Necklace
for Safety
Design a smart
necklace integrating
IoT and AI
IoT sensors, Cloud
Computing, Edge AI
Real-time alerts and
location tracking
improved safety
response
High dependency
on network
connectivity
Patel et al.
(2021)
Smart Wearable
Safety Device
for Women
Develop a wearable
safety device with
real-time
monitoring
IoT-based system,
GPS tracking, GSM
alert mechanism
Successfully sent
emergency alerts when
activated
Manual activation
required, lacks
AI-driven
automation
Johnson et
Enhancing
Wearable
Integrate federated
learning in smart
Federated Learning,
Improved personalized
safety detection while
Computational
load increased on
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue III, March 2025
www.ijltemas.in Page 424
al. (2021)
Safety Using
FL
wearables
Secure Aggregation
preserving privacy
edge devices
Zhao et al.
(2021)
FL-Based
Privacy
Enhancement
for Wearables
Ensure security in
wearable AI using
federated learning
Federated Learning,
Secure Enclaves
Improved security
without centralizing
user data
Requires
optimization for
low-power
devices
Wang &
Chen
(2020)
Machine
Learning for
Violence
Detection
Detect violent
situations using
wearable sensors
Deep Learning,
CNN, Audio and
Motion Analysis
ML models achieved
high accuracy in
detecting violent
behavior
False positives
due to ambiguous
motion patterns
Sharma &
Gupta
(2020)
AI-Powered
Wearable for
Women’s
Safety
Use AI for detecting
distress situations
Machine Learning,
Motion Sensors, NLP
for voice recognition
AI-based models
detected distress
patterns with 85%
accuracy
Cloud-based
model poses
privacy concerns
Silva et al.
(2020)
Integrating AI
and IoT for
Safety
Wearables
Develop an AI-
powered wearable
safety device
Deep Learning, IoT
Sensors, Biometric
Analysis
Detected anomalies in
heart rate and
movement to predict
danger
Needs
improvement in
false-positive
reduction
Mehta et al.
(2019)
Smart Wearable
for Women’s
Security
Implement a smart
device for
emergency alerts
IoT, GPS, GSM
Improved alert
transmission in
emergency situations
Lacks AI-based
automatic
detection
Raj et al.
(2019)
Emergency
Response
System for
Women’s
Safety
Implement an
emergency response
mechanism using
wearables
GSM, GPS, Mobile
App
Device successfully
sent alerts but required
user activation
Lacks AI-based
automatic distress
detection
Implementation Challenges
Despite its advantages, implementing FL in smart wearables comes with challenges:
1. Computational Power – Wearable devices have limited processing capabilities, requiring optimized models.
2. Battery Consumption – Continuous data processing and learning require efficient power management.
3. Network Constraints – FL relies on periodic model aggregation, which may be affected by connectivity issues.
Future work in this area will focus on lightweight AI models, improved battery efficiency, and hybrid cloud-edge computing
solutions to address these challenges.
V. Future Scope and Conclusion
The integration of federated learning in smart wearables like the proposed smart necklace presents a transformative approach to
women’s safety. Future advancements will focus on:
1. Enhancing AI Accuracy – By training on a diverse range of user environments, the necklace can improve threat
detection over time.
2. Extending Battery Life – Optimizing power usage to ensure prolonged functionality.
3. Integrating Multi-Modal Sensing – Combining visual, auditory, and motion sensors for a more comprehensive safety
response.
4. Improving Network Independence – Enabling offline learning and emergency triggers even in low-connectivity areas.
In conclusion, the smart necklace utilizing federated learning offers a promising and privacy-preserving solution to enhance
women’s security. By continuously improving its detection capabilities while ensuring user data protection, this wearable device
has the potential to significantly reduce response times in emergency situations, empowering women with real-time safety
assurance.
To further enhance personalization and user experience, the AI model within the smart pendant can be designed to adapt
seamlessly to individual behavior patterns without explicit user input. By incorporating natural language processing (NLP) for
interpreting voice-based commands and gesture recognition for detecting distress signals, the system can continuously learn and
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue III, March 2025
www.ijltemas.in Page 425
refine its detection capabilities in a passive, non-intrusive manner. This approach not only improves threat recognition accuracy
but also ensures that adaptation occurs organically, offering a smoother and more intelligent safety response.
References
1. Singh et al. (2023). "AI-Powered Edge Computing for Wearables." Focused on reducing latency and energy
consumption in AI wearables using Edge AI and Deep Learning.
2. Brown et al. (2023). "Wearable IoT for Women’s Safety." Explored IoT and cloud integration for location-based alerts.
3. Park & Kim (2023). "FL-Enhanced Smart Wearables." Investigated federated learning applications for personalized and
secure wearable AI.
4. Li et al. (2022). "Privacy-Preserving AI in Wearables." Utilized Federated Learning and Differential Privacy to improve
AI-based wearable security.
5. Kumar & Das (2022). "Real-Time Threat Assessment in Wearables." Developed AI-based motion recognition to detect
distress in wearables.
6. Ahmed & Malik (2022). "AI-Driven Safety Devices for Women." Implemented AI, NLP for voice commands, and
gesture recognition to improve real-time distress detection.
7. Kim & Lee (2021). "IoT-Based Smart Necklace for Safety." Designed an IoT-powered smart necklace integrating GPS
tracking and emergency response mechanisms.
8. Patel et al. (2021). "Smart Wearable Safety Device for Women." Developed an IoT-based system incorporating GPS and
GSM for real-time monitoring.
9. Johnson et al. (2021). "Enhancing Wearable Safety Using FL." Explored the role of federated learning in ensuring data
privacy in wearable safety devices.
10. Zhao et al. (2021). "FL-Based Privacy Enhancement for Wearables." Addressed privacy concerns in federated learning
with Secure Enclaves for wearable AI systems.
11. Wang & Chen (2020). "Machine Learning for Violence Detection." Applied deep learning and CNN models to wearable
sensor data for detecting violent situations.
12. Sharma & Gupta (2020). "AI-Powered Wearable for Women’s Safety." Used machine learning, motion sensors, and
NLP for distress recognition with 85% accuracy.
13. Silva et al. (2020). "Integrating AI and IoT for Safety Wearables." Designed a deep learning-based wearable device for
anomaly detection in biometric data.
14. Mehta et al. (2019). "Smart Wearable for Women’s Security." Developed an IoT, GPS, and GSM-based emergency alert
system for women’s safety.
15. Raj et al. (2019). "Emergency Response System for Women’s Safety." Proposed a wearable device with GSM and GPS
for distress signal transmission.
