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pro vyhledávání: '"Tam, Kahou"'
Federated Learning (FL) enables multiple devices to collaboratively train a shared model while preserving data privacy. Ever-increasing model complexity coupled with limited memory resources on the participating devices severely bottlenecks the deplo
Externí odkaz:
http://arxiv.org/abs/2410.11577
Autor:
Liao, Haicheng, Sun, Haoyu, Shen, Huanming, Wang, Chengyue, Tam, Kahou, Tian, Chunlin, Li, Li, Xu, Chengzhong, Li, Zhenning
Accurately and promptly predicting accidents among surrounding traffic agents from camera footage is crucial for the safety of autonomous vehicles (AVs). This task presents substantial challenges stemming from the unpredictable nature of traffic acci
Externí odkaz:
http://arxiv.org/abs/2407.17757
Autor:
Liao, Haicheng, Li, Yongkang, Wang, Chengyue, Guan, Yanchen, Tam, KaHou, Tian, Chunlin, Li, Li, Xu, Chengzhong, Li, Zhenning
As autonomous driving systems increasingly become part of daily transportation, the ability to accurately anticipate and mitigate potential traffic accidents is paramount. Traditional accident anticipation models primarily utilizing dashcam videos ar
Externí odkaz:
http://arxiv.org/abs/2407.16277
Federated Learning (FL) has evolved as a powerful tool for collaborative model training across multiple entities, ensuring data privacy in sensitive sectors such as healthcare and finance. However, the introduction of the Right to Be Forgotten (RTBF)
Externí odkaz:
http://arxiv.org/abs/2406.03078
Autor:
Liao, Haicheng, Li, Xuelin, Li, Yongkang, Kong, Hanlin, Wang, Chengyue, Wang, Bonan, Guan, Yanchen, Tam, KaHou, Li, Zhenning, Xu, Chengzhong
Trajectory prediction is a cornerstone in autonomous driving (AD), playing a critical role in enabling vehicles to navigate safely and efficiently in dynamic environments. To address this task, this paper presents a novel trajectory prediction model
Externí odkaz:
http://arxiv.org/abs/2405.02145
On-device training has become an increasingly popular approach to machine learning, enabling models to be trained directly on mobile and edge devices. However, a major challenge in this area is the limited memory available on these devices, which can
Externí odkaz:
http://arxiv.org/abs/2306.10388
Federated learning (FL) collaboratively trains a shared global model depending on multiple local clients, while keeping the training data decentralized in order to preserve data privacy. However, standard FL methods ignore the noisy client issue, whi
Externí odkaz:
http://arxiv.org/abs/2106.13239
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