2023
DOI: 10.1007/s40747-023-01239-5
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A federated pedestrian trajectory prediction model with data privacy protection

Rongrong Ni,
Yanan Lu,
Biao Yang
et al.

Abstract: Pedestrian trajectory prediction is essential for self-driving vehicles, social robots, and intelligent monitoring applications. Diverse trajectory data is critical for high-accuracy trajectory prediction. However, the trajectory data is captured in scattered scenes, which can cause the problem of data island. Furthermore, artificial aggregation of trajectory data suffers from the risk of data leakage, ignoring the rule of privacy protection. We propose a multi-scene federated trajectory prediction (Fed-TP) me… Show more

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Cited by 3 publications
(1 citation statement)
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“…In the realm of smart city services, a privacy-aware data fusion and prediction framework utilizing edge computing was developed, showing improved performance in terms of accuracy and computational efficiency [29][30][31]. Moreover, the use of federated learning in pedestrian trajectory prediction models has been explored, offering better data privacy security and prediction performance [32][33][34][35]. The development of medical sports data privacy protection methods based on legal risk control highlights the importance of standardizing data handling practices in the medical field [36].…”
Section: Related Workmentioning
confidence: 99%
“…In the realm of smart city services, a privacy-aware data fusion and prediction framework utilizing edge computing was developed, showing improved performance in terms of accuracy and computational efficiency [29][30][31]. Moreover, the use of federated learning in pedestrian trajectory prediction models has been explored, offering better data privacy security and prediction performance [32][33][34][35]. The development of medical sports data privacy protection methods based on legal risk control highlights the importance of standardizing data handling practices in the medical field [36].…”
Section: Related Workmentioning
confidence: 99%