SimDeep: Federated 3D Indoor Localization via Similarity-Aware Aggregation
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911088322281472 |
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| author | Jaheen, Ahmed Elsamanody, Sarah Rizk, Hamada Youssef, Moustafa |
| author_facet | Jaheen, Ahmed Elsamanody, Sarah Rizk, Hamada Youssef, Moustafa |
| contents | Indoor localization plays a pivotal role in supporting a wide array of location-based services, including navigation, security, and context-aware computing within intricate indoor environments. Despite considerable advancements, deploying indoor localization systems in real-world scenarios remains challenging, largely because of non-independent and identically distributed (non-IID) data and device heterogeneity. In response, we propose SimDeep, a novel Federated Learning (FL) framework explicitly crafted to overcome these obstacles and effectively manage device heterogeneity. SimDeep incorporates a Similarity Aggregation Strategy, which aggregates client model updates based on data similarity, significantly alleviating the issues posed by non-IID data. Our experimental evaluations indicate that SimDeep achieves an impressive accuracy of 92.89%, surpassing traditional federated and centralized techniques, thus underscoring its viability for real-world deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01515 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SimDeep: Federated 3D Indoor Localization via Similarity-Aware Aggregation Jaheen, Ahmed Elsamanody, Sarah Rizk, Hamada Youssef, Moustafa Machine Learning Indoor localization plays a pivotal role in supporting a wide array of location-based services, including navigation, security, and context-aware computing within intricate indoor environments. Despite considerable advancements, deploying indoor localization systems in real-world scenarios remains challenging, largely because of non-independent and identically distributed (non-IID) data and device heterogeneity. In response, we propose SimDeep, a novel Federated Learning (FL) framework explicitly crafted to overcome these obstacles and effectively manage device heterogeneity. SimDeep incorporates a Similarity Aggregation Strategy, which aggregates client model updates based on data similarity, significantly alleviating the issues posed by non-IID data. Our experimental evaluations indicate that SimDeep achieves an impressive accuracy of 92.89%, surpassing traditional federated and centralized techniques, thus underscoring its viability for real-world deployment. |
| title | SimDeep: Federated 3D Indoor Localization via Similarity-Aware Aggregation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.01515 |