用相似性聚合提升联邦学习在异构室内定位中的精度
SimDeep: Federated 3D Indoor Localization via Similarity-Aware Aggregation
- 根据数据相似性聚合客户端模型更新，缓解非独立同分布问题
- 实验显示定位准确率达92.89%，优于传统联邦与集中式方法
- 适合部署在设备异构、数据分布不均的真实室内场景
室内定位在导航、安全和情境感知计算等复杂室内环境中发挥关键作用。尽管取得显著进展，实际部署仍面临非独立同分布（non-IID）数据和设备异构性的挑战。为此，我们提出SimDeep，一种专为应对这些障碍设计的新型联邦学习（FL）框架。SimDeep引入相似性聚合策略，基于数据相似性聚合客户端模型更新，有效缓解非IID数据带来的问题。实验表明，SimDeep实现92.89%的高精度，超越传统联邦与集中式方法，验证了其在真实场景部署的可行性。
原文摘要 · Abstract (English)
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.
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