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However, centralizing facial data raises ",["$","strong",null,{"children":"serious privacy concerns"}],"."]}],"\n",["$","$L8",null,{"style":{"lineHeight":"175%"},"variant":"body-default-m","onBackground":"neutral-medium","marginTop":"8","marginBottom":"12","children":[["$","strong",null,{"children":"Federated Learning (FL)"}]," allows multiple clients (e.g., user devices or edge servers) to collaboratively train a global machine learning model ",["$","strong",null,{"children":"without sharing raw data"}],", making it an ideal solution for privacy-preserving systems."]}],"\n",["$","$L8",null,{"style":{"lineHeight":"175%"},"variant":"body-default-m","onBackground":"neutral-medium","marginTop":"8","marginBottom":"12","children":["This project proposes a ",["$","strong",null,{"children":"federated learning-based facial recognition system"}]," for attendance tracking, which ensures user privacy while maintaining high accuracy in recognition."]}],"\n",["$","hr",null,{}],"\n",["$","$L16",null,{"style":{"marginTop":"var(--static-space-24)","marginBottom":"var(--static-space-12)"},"level":2,"id":"motivation","children":"Motivation"}],"\n",["$","ul",null,{"children":["\n",["$","li",null,{"children":[["$","strong",null,{"children":"Privacy Preservation:"}]," Sensitive facial images remain on local devices."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Data Security:"}]," Reduces risk of data breaches by avoiding centralized storage."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Compliance:"}]," Aligns with regulations like GDPR, HIPAA, and other privacy laws."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Decentralized Learning:"}]," Enables scalable training across multiple devices."]}],"\n"]}],"\n",["$","hr",null,{}],"\n",["$","$L16",null,{"style":{"marginTop":"var(--static-space-24)","marginBottom":"var(--static-space-12)"},"level":2,"id":"background","children":"Background"}],"\n",["$","$L16",null,{"style":{"marginTop":"var(--static-space-24)","marginBottom":"var(--static-space-12)"},"level":3,"id":"facial-recognition","children":"Facial Recognition"}],"\n",["$","$L8",null,{"style":{"lineHeight":"175%"},"variant":"body-default-m","onBackground":"neutral-medium","marginTop":"8","marginBottom":"12","children":"Facial recognition involves detecting, encoding, and matching faces in images or video frames. Typical steps include:"}],"\n",["$","ol",null,{"children":["\n",["$","li",null,{"children":[["$","strong",null,{"children":"Face Detection:"}]," Identify faces using methods like Haar Cascades, MTCNN, or YOLO."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Feature Extraction:"}]," Convert detected faces into numerical embeddings using CNNs (e.g., FaceNet, ArcFace)."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Matching and Classification:"}]," Compare embeddings with registered user database to identify individuals."]}],"\n"]}],"\n",["$","$L16",null,{"style":{"marginTop":"var(--static-space-24)","marginBottom":"var(--static-space-12)"},"level":3,"id":"privacy-concerns","children":"Privacy Concerns"}],"\n",["$","ul",null,{"children":["\n",["$","li",null,{"children":["Centralized databases of facial images are vulnerable to ",["$","strong",null,{"children":"hacking, misuse, and leaks"}],"."]}],"\n",["$","li",null,{"children":"Sharing raw data across servers violates user privacy."}],"\n",["$","li",null,{"children":"Regulatory compliance demands minimal exposure of personal data."}],"\n"]}],"\n",["$","$L16",null,{"style":{"marginTop":"var(--static-space-24)","marginBottom":"var(--static-space-12)"},"level":3,"id":"federated-learning","children":"Federated Learning"}],"\n",["$","$L8",null,{"style":{"lineHeight":"175%"},"variant":"body-default-m","onBackground":"neutral-medium","marginTop":"8","marginBottom":"12","children":"Federated Learning is a decentralized ML approach where:"}],"\n",["$","ul",null,{"children":["\n",["$","li",null,{"children":"Each client trains a local model using its own data."}],"\n",["$","li",null,{"children":["Clients share ",["$","strong",null,{"children":"model updates (gradients or weights)"}]," instead of raw data."]}],"\n",["$","li",null,{"children":"A central server aggregates updates to form a global model."}],"\n",["$","li",null,{"children":"The global model is redistributed to clients iteratively."}],"\n"]}],"\n",["$","$L8",null,{"style":{"lineHeight":"175%"},"variant":"body-default-m","onBackground":"neutral-medium","marginTop":"8","marginBottom":"12","children":["$","strong",null,{"children":"Advantages for Facial Recognition:"}]}],"\n",["$","ul",null,{"children":["\n",["$","li",null,{"children":"No raw images leave the client device."}],"\n",["$","li",null,{"children":"Training is distributed, reducing server computational load."}],"\n",["$","li",null,{"children":"Enables continuous learning without compromising privacy."}],"\n"]}],"\n",["$","hr",null,{}],"\n",["$","$L16",null,{"style":{"marginTop":"var(--static-space-24)","marginBottom":"var(--static-space-12)"},"level":2,"id":"system-architecture","children":"System Architecture"}],"\n",["$","pre",null,{"children":["$","code",null,{"className":"language-bash","children":"  Client Devices (Edge Nodes)\n  │\n  ├── Local Face Detection & Recognition Model\n  ├── Local Training with Private Data\n  │\n  └── Sends Model Updates → Central Server (Federated Aggregator)\n  │\n  ├── Aggregates Weights (FedAvg / FedProx)\n  └── Updates Global Model\n  │\n  └── Distributes Updated Model to Clients\n"}]}],"\n",["$","ul",null,{"children":["\n",["$","li",null,{"children":[["$","strong",null,{"children":"Client Devices:"}]," Smartphones, tablets, or local terminals where facial data is captured."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Federated Aggregator:"}]," Server aggregates model weights and coordinates training rounds."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Global Model:"}]," Continuously improved facial recognition model shared with all clients."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Attendance Logging:"}]," After identification, attendance is recorded in a secure database."]}],"\n"]}],"\n",["$","hr",null,{}],"\n",["$","$L16",null,{"style":{"marginTop":"var(--static-space-24)","marginBottom":"var(--static-space-12)"},"level":2,"id":"implementation-details","children":"Implementation Details"}],"\n",["$","$L16",null,{"style":{"marginTop":"var(--static-space-24)","marginBottom":"var(--static-space-12)"},"level":3,"id":"1-local-model-training","children":"1. Local Model Training"}],"\n",["$","ul",null,{"children":["\n",["$","li",null,{"children":"Each client device collects facial images of users locally."}],"\n",["$","li",null,{"children":"Preprocessing includes resizing, normalization, and augmentation."}],"\n",["$","li",null,{"children":["Local training uses a ",["$","strong",null,{"children":"CNN-based model"}]," (e.g., MobileFaceNet for efficiency)."]}],"\n",["$","li",null,{"children":["Loss function: ",["$","strong",null,{"children":"Cross-Entropy"}]," or ",["$","strong",null,{"children":"Triplet Loss"}]," for embeddings."]}],"\n"]}],"\n",["$","$L16",null,{"style":{"marginTop":"var(--static-space-24)","marginBottom":"var(--static-space-12)"},"level":3,"id":"2-federated-aggregation","children":"2. Federated Aggregation"}],"\n",["$","ul",null,{"children":["\n",["$","li",null,{"children":"Model weights are transmitted (not raw data) to the central server."}],"\n",["$","li",null,{"children":["Aggregation strategies:","\n",["$","ul",null,{"children":["\n",["$","li",null,{"children":[["$","strong",null,{"children":"FedAvg:"}]," Weighted averaging of local model updates."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"FedProx:"}]," Handles non-IID data across clients."]}],"\n"]}],"\n"]}],"\n",["$","li",null,{"children":"After aggregation, the updated global model is redistributed."}],"\n"]}],"\n",["$","$L16",null,{"style":{"marginTop":"var(--static-space-24)","marginBottom":"var(--static-space-12)"},"level":3,"id":"3-model-deployment","children":"3. Model Deployment"}],"\n",["$","ul",null,{"children":["\n",["$","li",null,{"children":"The global model is deployed on client devices for inference."}],"\n",["$","li",null,{"children":["Edge inference ensures ",["$","strong",null,{"children":"low latency"}]," for real-time attendance recognition."]}],"\n",["$","li",null,{"children":"Model updates are periodic or triggered by new data availability."}],"\n"]}],"\n",["$","$L16",null,{"style":{"marginTop":"var(--static-space-24)","marginBottom":"var(--static-space-12)"},"level":3,"id":"4-attendance-logging","children":"4. Attendance Logging"}],"\n",["$","ul",null,{"children":["\n",["$","li",null,{"children":"Recognized faces trigger secure logging of attendance."}],"\n",["$","li",null,{"children":["Optional integration with ",["$","strong",null,{"children":"Google Sheets"}],", ",["$","strong",null,{"children":"SQL databases"}],", or ",["$","strong",null,{"children":"cloud storage"}],"."]}],"\n",["$","li",null,{"children":"All logs exclude sensitive image data, storing only anonymized identifiers and timestamps."}],"\n"]}],"\n",["$","hr",null,{}],"\n",["$","$L16",null,{"style":{"marginTop":"var(--static-space-24)","marginBottom":"var(--static-space-12)"},"level":2,"id":"evaluation-metrics","children":"Evaluation Metrics"}],"\n",["$","ul",null,{"children":["\n",["$","li",null,{"children":[["$","strong",null,{"children":"Accuracy:"}]," Correct identification of faces."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Precision, Recall, F1-Score:"}]," For each class/user."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Communication Efficiency:"}]," Bytes transferred per training round."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Latency:"}]," Time for recognition and attendance logging."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Privacy Metrics:"}]," Ensuring no raw facial images leave client devices."]}],"\n"]}],"\n",["$","hr",null,{}],"\n",["$","$L16",null,{"style":{"marginTop":"var(--static-space-24)","marginBottom":"var(--static-space-12)"},"level":2,"id":"use-cases","children":"Use Cases"}],"\n",["$","ol",null,{"children":["\n",["$","li",null,{"children":[["$","strong",null,{"children":"Educational Institutions:"}]," Secure and automated student attendance."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Corporate Offices:"}]," Privacy-preserving employee attendance monitoring."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Event Management:"}]," Tracking participants without centralizing sensitive data."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Healthcare Facilities:"}]," Staff identification without compromising patient data privacy."]}],"\n"]}],"\n",["$","hr",null,{}],"\n",["$","$L16",null,{"style":{"marginTop":"var(--static-space-24)","marginBottom":"var(--static-space-12)"},"level":2,"id":"conclusion","children":"Conclusion"}],"\n",["$","$L8",null,{"style":{"lineHeight":"175%"},"variant":"body-default-m","onBackground":"neutral-medium","marginTop":"8","marginBottom":"12","children":["The ",["$","strong",null,{"children":"federated learning-based facial recognition system"}]," provides a ",["$","strong",null,{"children":"privacy-preserving, decentralized solution"}]," for attendance tracking. By keeping sensitive facial data local and only sharing model updates, the system ensures security, scalability, and compliance with privacy regulations while maintaining high recognition accuracy."]}],"\n",["$","$L8",null,{"style":{"lineHeight":"175%"},"variant":"body-default-m","onBackground":"neutral-medium","marginTop":"8","marginBottom":"12","children":["This framework demonstrates that ",["$","strong",null,{"children":"privacy and AI can coexist"}],", enabling modern attendance systems without sacrificing personal data protection."]}],"\n",["$","hr",null,{}],"\n",["$","$L16",null,{"style":{"marginTop":"var(--static-space-24)","marginBottom":"var(--static-space-12)"},"level":2,"id":"references","children":"References"}],"\n",["$","ol",null,{"children":["\n",["$","li",null,{"children":["Kairouz, P., McMahan, H. B., et al., ",["$","em",null,{"children":"Advances and Open Problems in Federated Learning"}],", Foundations and Trends in Machine Learning, 2021."]}],"\n",["$","li",null,{"children":["Schroff, F., Kalenichenko, D., & Philbin, J., ",["$","em",null,{"children":"FaceNet: A Unified Embedding for Face Recognition and Clustering"}],", CVPR, 2015."]}],"\n",["$","li",null,{"children":["Bonawitz, K., Eichner, H., et al., ",["$","em",null,{"children":"Federated Learning: Challenges, Methods, and Future Directions"}],", IEEE, 2019."]}],"\n",["$","li",null,{"children":["McMahan, H. B., et al., ",["$","em",null,{"children":"Communication-Efficient Learning of Deep Networks from Decentralized Data"}],", AISTATS, 2017."]}],"\n",["$","li",null,{"children":["Google AI Blog, ",["$","em",null,{"children":"Federated Learning for On-Device AI"}],", 2017."]}],"\n",["$","li",null,{"children":["ArcFace: Deng, Jiankang, et al., ",["$","em",null,{"children":"ArcFace: Additive Angular Margin Loss for Deep Face Recognition"}],", CVPR, 2019."]}],"\n"]}]]
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