.png){kind=icon}
返回
FedTrack: A Collaborative Target Tracking Framework Based on Adaptive Federated Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/TVT.2024.3395292.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Tracking mobile targets is a basic task for many applications, e.g., wildlife protection, area surveillance, battlefield reconnaissance, disaster rescue, etc. In these cases, multiple edge devices are usually deployed to collect images or videos from the target area. Two mainstream tracking methodologies have been proposed to recognize and track targets from such data sources cooperatively. The centralized strategies collect data from the edge devices and thereafter run mining and learning algorithms upon such data. On the contrary, the distributed strategies implement such algorithms in a distributed manner. However, these methods incur either high transmission costs or slow convergence speeds. To this end, this paper presents a novel cooperative tracking framework (i.e., FedTrack) based on adaptive federated learning. A dual reputation mechanism has been formulated, and subsequently, an adaptive node selection algorithm has been suggested to ascertain the nodes suitable for involvement in the training process. Furthermore, a strategy for selecting the aggregation node based on capability has been developed to enhance the efficiency of aggregation. As far as we know, FedTrack is the first federated learning framework for collaborative tracking. Experimental results demonstrate that FedTrack achieves comparable or even better accuracy than state-of-the-art methods, yet needs much fewer data transmission costs and much less time consumption.
Keyword:
Training
Target tracking
Collaboration
Federated learning
Data models
Computational modeling
Servers
Adaptive node selection
collaborative target tracking
federated learning
reputation value
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
机构
引用论文
Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review资源受限边缘设备上深度学习推理的高效加速: 综述.png)
PROCEEDINGS OF THE IEEE
OAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAI分享收藏分享收藏
Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective无线联合学习网络中的客户端选择和带宽分配: 长期观点OAAI
OAAI分享收藏
查看更多内容