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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.20095517 |
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| _version_ | 1866901577418145792 |
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| author | Monika Sharma, Nidhi Sharma, Pushpendra Kumar Dwivedi |
| author_facet | Monika Sharma, Nidhi Sharma, Pushpendra Kumar Dwivedi |
| contents | <p>Detecting Autism Spectrum Disorder (ASD) at an early stage is critically important for child development <br>and therapeutic intervention. However, building reliable artificial intelligence models for ASD screening <br>is hindered by strict data privacy regulations, limited availability of labeled facial image datasets, and <br>significant variations in facial characteristics across different regions and cultures. In this work, we <br>propose a federated learning (FL) framework for ASD detection using facial images, wherein raw data <br>remains locally stored at participating clinical centers and only trained model parameters are shared with <br>a central aggregation server. Each client performs standardized preprocessing including face extraction <br>via MediaPipe, removal of low-quality and duplicate images, and histogram-based normalization to <br>mitigate lighting and contrast variations. We systematically compare three convolutional neural network <br>(CNN) architectures—MobileNetV2, EfficientNetB0, and EfficientNetB4—trained in a federated setting <br>using the Federated Averaging (FedAvg) algorithm. EfficientNetB4 achieved the highest global model <br>accuracy of 91.7% and an F1-score of 90.3% after 20 federation rounds. The results demonstrate that <br>combining federated learning with careful client-side preprocessing significantly improves detection <br>accuracy while fully preserving data privacy. The proposed framework supports cross-cultural <br>applicability and is suitable for deployment in real-world clinical decision-support systems. </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20095517 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Cross-Cultural Autism Spectrum Disorder Detection Using Privacy Preserving Federated Facial Analysis Monika Sharma, Nidhi Sharma, Pushpendra Kumar Dwivedi <p>Detecting Autism Spectrum Disorder (ASD) at an early stage is critically important for child development <br>and therapeutic intervention. However, building reliable artificial intelligence models for ASD screening <br>is hindered by strict data privacy regulations, limited availability of labeled facial image datasets, and <br>significant variations in facial characteristics across different regions and cultures. In this work, we <br>propose a federated learning (FL) framework for ASD detection using facial images, wherein raw data <br>remains locally stored at participating clinical centers and only trained model parameters are shared with <br>a central aggregation server. Each client performs standardized preprocessing including face extraction <br>via MediaPipe, removal of low-quality and duplicate images, and histogram-based normalization to <br>mitigate lighting and contrast variations. We systematically compare three convolutional neural network <br>(CNN) architectures—MobileNetV2, EfficientNetB0, and EfficientNetB4—trained in a federated setting <br>using the Federated Averaging (FedAvg) algorithm. EfficientNetB4 achieved the highest global model <br>accuracy of 91.7% and an F1-score of 90.3% after 20 federation rounds. The results demonstrate that <br>combining federated learning with careful client-side preprocessing significantly improves detection <br>accuracy while fully preserving data privacy. The proposed framework supports cross-cultural <br>applicability and is suitable for deployment in real-world clinical decision-support systems. </p> |
| title | Cross-Cultural Autism Spectrum Disorder Detection Using Privacy Preserving Federated Facial Analysis |
| url | https://doi.org/10.5281/zenodo.20095517 |