Zobrazeno 1 - 10
of 88
pro vyhledávání: '"Li, Zonghang"'
Autor:
Tao, Zhuofu, Shi, Yichen, Huo, Yiru, Ye, Rui, Li, Zonghang, Huang, Li, Wu, Chen, Bai, Na, Yu, Zhiping, Lin, Ting-Jung, He, Lei
Today's analog/mixed-signal (AMS) integrated circuit (IC) designs demand substantial manual intervention. The advent of multimodal large language models (MLLMs) has unveiled significant potential across various fields, suggesting their applicability
Externí odkaz:
http://arxiv.org/abs/2405.09045
Autor:
Li, Zonghang, Feng, Wenjiao, Cai, Weibo, Yu, Hongfang, Luo, Long, Sun, Gang, Du, Hongyang, Niyato, Dusit
Distributed machine learning is becoming increasingly popular for geo-distributed data analytics, facilitating the collaborative analysis of data scattered across data centers in different regions. This paradigm eliminates the need for centralizing s
Externí odkaz:
http://arxiv.org/abs/2404.11352
Autor:
Du, Hongyang, Zhang, Ruichen, Liu, Yinqiu, Wang, Jiacheng, Lin, Yijing, Li, Zonghang, Niyato, Dusit, Kang, Jiawen, Xiong, Zehui, Cui, Shuguang, Ai, Bo, Zhou, Haibo, Kim, Dong In
Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distribu
Externí odkaz:
http://arxiv.org/abs/2308.05384
Since the advent of the Internet of Things (IoT), exchanging vast amounts of information has increased the number of security threats in networks. As a result, intrusion detection based on deep learning (DL) has been developed to achieve high through
Externí odkaz:
http://arxiv.org/abs/2306.03707
Decentralized, tamper-proof blockchain is regarded as a solution to a challenging authentication issue in the Internet of Vehicles (IoVs). However, the consensus time and communication overhead of blockchain increase significantly as the number of ve
Externí odkaz:
http://arxiv.org/abs/2305.17377
Autor:
Du, Hongyang, Li, Zonghang, Niyato, Dusit, Kang, Jiawen, Xiong, Zehui, Huang, Huawei, Mao, Shiwen
As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AIGenerated Content (AIGC) emerges as a key solution, yet the resource intensive nature of large Generative AI (GAI) models prese
Externí odkaz:
http://arxiv.org/abs/2303.13052
Autor:
Du, Hongyang, Li, Zonghang, Niyato, Dusit, Kang, Jiawen, Xiong, Zehui, Xuemin, Shen, Kim, Dong In
Artificial Intelligence-Generated Content (AIGC) refers to the use of AI to automate the information creation process while fulfilling the personalized requirements of users. However, due to the instability of AIGC models, e.g., the stochastic nature
Externí odkaz:
http://arxiv.org/abs/2301.03220
Federated Learning (FL), as a rapidly evolving privacy-preserving collaborative machine learning paradigm, is a promising approach to enable edge intelligence in the emerging Industrial Metaverse. Even though many successful use cases have proved the
Externí odkaz:
http://arxiv.org/abs/2211.03300
Semantic communication, as a promising technology, has emerged to break through the Shannon limit, which is envisioned as the key enabler and fundamental paradigm for future 6G networks and applications, e.g., smart healthcare. In this paper, we focu
Externí odkaz:
http://arxiv.org/abs/2209.12274
Federated learning enables isolated clients to train a shared model collaboratively by aggregating the locally-computed gradient updates. However, privacy information could be leaked from uploaded gradients and be exposed to malicious attackers or an
Externí odkaz:
http://arxiv.org/abs/2205.13216