Zobrazeno 1 - 10
of 764
pro vyhledávání: '"Huo, Wei"'
Decentralized optimization has become a standard paradigm for solving large-scale decision-making problems and training large machine learning models without centralizing data. However, this paradigm introduces new privacy and security risks, with ma
Externí odkaz:
http://arxiv.org/abs/2408.08628
This paper investigates the use of the cubic-regularized Newton method within a federated learning framework while addressing two major concerns that commonly arise in federated learning: privacy leakage and communication bottleneck. We introduce a f
Externí odkaz:
http://arxiv.org/abs/2408.04315
Autor:
Huo, Wei, Yang, Huiwen, Yang, Nachuan, Yang, Zhaohua, Zhang, Jiuzhou, Nan, Fuhai, Chen, Xingzhou, Mao, Yifan, Hu, Suyang, Wang, Pengyu, Zheng, Xuanyu, Zhao, Mingming, Shi, Ling
The advent of next-generation wireless communication systems heralds an era characterized by high data rates, low latency, massive connectivity, and superior energy efficiency. These systems necessitate innovative and adaptive strategies for resource
Externí odkaz:
http://arxiv.org/abs/2408.02943
The distributed computation of a Nash equilibrium (NE) for non-cooperative games is gaining increased attention recently. Due to the nature of distributed systems, privacy and communication efficiency are two critical concerns. Traditional approaches
Externí odkaz:
http://arxiv.org/abs/2405.04757
Compression-based Privacy Preservation for Distributed Nash Equilibrium Seeking in Aggregative Games
This paper explores distributed aggregative games in multi-agent systems. Current methods for finding distributed Nash equilibrium require players to send original messages to their neighbors, leading to communication burden and privacy issues. To jo
Externí odkaz:
http://arxiv.org/abs/2405.03106
This paper investigates privacy issues in distributed resource allocation over directed networks, where each agent holds a private cost function and optimizes its decision subject to a global coupling constraint through local interaction with other a
Externí odkaz:
http://arxiv.org/abs/2403.18275
Distributed Nash equilibrium (NE) seeking problems for networked games have been widely investigated in recent years. Despite the increasing attention, communication expenditure is becoming a major bottleneck for scaling up distributed approaches wit
Externí odkaz:
http://arxiv.org/abs/2311.13994
Autor:
Shi, Jingyi, Xiao, Yang, Li, Yuekang, Li, Yeting, Yu, Dongsong, Yu, Chendong, Su, Hui, Chen, Yufeng, Huo, Wei
Deep learning (DL) applications are prevalent nowadays as they can help with multiple tasks. DL libraries are essential for building DL applications. Furthermore, DL operators are the important building blocks of the DL libraries, that compute the mu
Externí odkaz:
http://arxiv.org/abs/2305.17914
In this paper, we study the problem of consensus-based distributed Nash equilibrium (NE) seeking where a network of players, abstracted as a directed graph, aim to minimize their own local cost functions non-cooperatively. Considering the limited ene
Externí odkaz:
http://arxiv.org/abs/2304.10338
A visual single-object tracker is an indispensable component of underwater vehicles (UVs) in marine organism grasping tasks. Its accuracy and stability are imperative to guide the UVs to perform grasping behavior. Although single-object trackers show
Externí odkaz:
http://arxiv.org/abs/2301.01482