Zobrazeno 1 - 10
of 417
pro vyhledávání: '"Zhang, Yaoxue"'
Autor:
Wang, Tuowei, Fan, Ruwen, Huang, Minxing, Hao, Zixu, Li, Kun, Cao, Ting, Lu, Youyou, Zhang, Yaoxue, Ren, Ju
Large Language Models (LLMs) have achieved remarkable success across various domains, yet deploying them on mobile devices remains an arduous challenge due to their extensive computational and memory demands. While lightweight LLMs have been develope
Externí odkaz:
http://arxiv.org/abs/2410.19274
In this paper, we study offline-to-online Imitation Learning (IL) that pretrains an imitation policy from static demonstration data, followed by fast finetuning with minimal environmental interaction. We find the na\"ive combination of existing offli
Externí odkaz:
http://arxiv.org/abs/2405.17477
Offline Imitation Learning (IL) with imperfect demonstrations has garnered increasing attention owing to the scarcity of expert data in many real-world domains. A fundamental problem in this scenario is how to extract positive behaviors from noisy da
Externí odkaz:
http://arxiv.org/abs/2405.17476
Image data have been extensively used in Deep Neural Network (DNN) tasks in various scenarios, e.g., autonomous driving and medical image analysis, which incurs significant privacy concerns. Existing privacy protection techniques are unable to effici
Externí odkaz:
http://arxiv.org/abs/2404.04098
Autor:
Xu, Yang, Tan, Yunlin, Zhang, Cheng, Chi, Kai, Sun, Peng, Yang, Wenyuan, Ren, Ju, Jiang, Hongbo, Zhang, Yaoxue
Embedding watermarks into models has been widely used to protect model ownership in federated learning (FL). However, existing methods are inadequate for protecting the ownership of personalized models acquired by clients in personalized FL (PFL). Th
Externí odkaz:
http://arxiv.org/abs/2402.19054
While large language models (LLMs) are empowered with broad knowledge, their task-specific performance is often suboptimal. It necessitates fine-tuning LLMs with task-specific data, but such data may be inaccessible due to privacy concerns. In this p
Externí odkaz:
http://arxiv.org/abs/2312.05842
Traditional executable delivery models pose challenges for IoT devices with limited storage, necessitating the download of complete executables and dependencies. Network solutions like NFS, designed for data files, encounter high IO overhead for irre
Externí odkaz:
http://arxiv.org/abs/2312.04871
User scheduling and beamforming design are two crucial yet coupled topics for wireless communication systems. They are usually optimized separately with conventional optimization methods. In this paper, a novel cross-layer optimization problem is con
Externí odkaz:
http://arxiv.org/abs/2203.00934
In wireless network, the optimization problems generally have complex constraints, and are usually solved via utilizing the traditional optimization methods that have high computational complexity and need to be executed repeatedly with the change of
Externí odkaz:
http://arxiv.org/abs/2201.08994
Federated meta-learning (FML) has emerged as a promising paradigm to cope with the data limitation and heterogeneity challenges in today's edge learning arena. However, its performance is often limited by slow convergence and corresponding low commun
Externí odkaz:
http://arxiv.org/abs/2108.06453