用注意力机制的联邦学习，高效检测城市行人跌倒
FLAMe: Federated Learning with Attention Mechanism using Spatio-Temporal Keypoint Transformers for Pedestrian Fall Detection in Smart Cities
- 基于关键点变换器与注意力机制的联邦学习框架
- 94.02%准确率，通信量减少40%优于传统方法
- 适合注重隐私与效率的城市公共安全场景
在智慧城市建设中，行人跌倒检测对保障市民安全与生活质量至关重要。本文提出一种新型跌倒检测系统FLAMe（Federated Learning with Attention Mechanism），结合联邦学习（FL）与轻量级时空关键点变换器模型，仅上传关键权重信息，有效降低通信开销并保护数据隐私。实验基于AI-Hub提供的‘Fall Accident Risk Behavior Video-Sensor Pair data’数据集，包含22,672个视频样本。结果表明，该系统在约19万次参数传输下达到94.02%准确率，性能接近集中式学习，且相比现有联邦平均算法FedAvg，通信成本降低约40%。验证了FLAMe在智慧城市分布式环境中的鲁棒性与实用性。
原文摘要 · Abstract (English)
In smart cities, detecting pedestrian falls is a major challenge to ensure the safety and quality of life of citizens. In this study, we propose a novel fall detection system using FLAMe (Federated Learning with Attention Mechanism), a federated learning (FL) based algorithm. FLAMe trains around important keypoint information and only transmits the trained important weights to the server, reducing communication costs and preserving data privacy. Furthermore, the lightweight keypoint transformer model is integrated into the FL framework to effectively learn spatio-temporal features. We validated the experiment using 22,672 video samples from the "Fall Accident Risk Behavior Video-Sensor Pair data" dataset from AI-Hub. As a result of the experiment, the FLAMe-based system achieved an accuracy of 94.02% with about 190,000 transmission parameters, maintaining performance similar to that of existing centralized learning while maximizing efficiency by reducing communication costs by about 40% compared to the existing FL algorithm, FedAvg. Therefore, the FLAMe algorithm has demonstrated that it provides robust performance in the distributed environment of smart cities and is a practical and effective solution for public safety.
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