no code implementations • 8 May 2024 • Xiaomeng Chen, Wei Huo, Kemi Ding, Subhrakanti Dey, Ling Shi
Due to the nature of distributed systems, privacy and communication efficiency are two critical concerns.
no code implementations • 6 May 2024 • Wei Huo, Xiaomeng Chen, Kemi Ding, Subhrakanti Dey, Ling Shi
To jointly address these issues, we propose an algorithm that uses stochastic compression to save communication resources and conceal information through random errors induced by compression.
no code implementations • 27 Mar 2024 • Wei Huo, Xiaomeng Chen, Lingying Huang, Karl Henrik Johansson, Ling Shi
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 agents.
no code implementations • 23 Nov 2023 • Xiaomeng Chen, Wei Huo, Yuchi Wu, Subhrakanti Dey, Ling Shi
We demonstrate that SETC-DNES guarantees linear convergence to the NE while achieving even greater reductions in communication costs compared to ETC-DNES.
no code implementations • 20 Apr 2023 • Wei Huo, Kam Fai Elvis Tsang, Yamin Yan, Karl Henrik Johansson, Ling Shi
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.
2 code implementations • 4 Jan 2023 • Yunfeng Li, Bo wang, Ye Li, Zhuoyan Liu, Wei Huo, Yueming Li, Jian Cao
The UOHT training paradigm is designed to train the sample-imbalanced underwater tracker so that the tracker is exposed to a great number of underwater domain training samples and learns the feature expressions.
no code implementations • 5 Nov 2021 • Lingying Huang, Xiaomeng Chen, Wei Huo, Jiazheng Wang, Fan Zhang, Bo Bai, Ling Shi
In order to improve the speed of B&B algorithms, learning techniques have been introduced in this algorithm recently.