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检索条件"主题词=distributed algorithms"

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Joint Communication and Computation Scheduling for MEC-Enabled AIGC Services Based on Generative Diffusion Model

Joint Communication and Computation Scheduling for MEC-Enabl...

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International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

作者： Huaizhe Liu Jiaqi Wu Xinyi Zhuang Hongjia Wu Lin Gao Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China

ISBN: (数字)9783903176652

ISBN: (纸本)9798331508722

Artificial Intelligence-Generated Content (AIGC) based on Generative Diffusion Model (GDM) has emerged as a promising paradigm of content generation, revolutionizing the creation of diverse contents and driving significant technological advancements. Due to the low latency requirements of AIGC services, mobile edge computing (MEC) has become a crucial enabling technology for these services. In this work, we consider an MEC-enabled GDM-based AIGC network, which consists of multiple GDMs with varying sizes and capabilities deployed on edge computing servers (ES), and multiple mobile users (UEs) with diverse latency and accuracy requirements requesting AIGC services from ES through wireless access points (APs). In such a scenario, we are interested in the joint communication and computation scheduling problem for UEs, which involves selecting the appropriate APs (along with the communication bandwidth allocation) and the appropriate ES (together with the computation resource allocation and model inference optimization) for UEs, considering both the UEs' heterogeneous requirements and the GDMs' heterogeneous capabilities. To address the problem in a practical scenario with decentralized, autonomous, and self-interested UEs, we formulate a non-cooperative game, called the Joint User Association and Computation Offloading (JUACO) game, where each UE acts as a game player, selecting the best AP (for communication) as well as the best ES and the best GDM model inference step (for computation), aiming to minimize the inference time while meeting the specified inference accuracy requirement. We prove that the proposed JUACO game is a potential game, thus guaranteeing the existence of Nash equilibrium (NE) and the convergence of simple best response-based distributed algorithms to NE. Simulation results demonstrate that the proposed JDACO game approach significantly reduces the inference time and meets the required accuracy compared to traditional methods, validating the effec

关键词： Accuracy Processor scheduling Computational modeling Wireless networks Games Wireless access points Nash equilibrium Diffusion models distributed algorithms Optimization

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distributed Resource Management for Multi-node Aggregated Satellite Edge Computing in Satellite-Terrestrial Integrated Internet of Vehicles

Distributed Resource Management for Multi-node Aggregated Sa...

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Satellite Computing (Satellite), IEEE International Conference on

作者： Qi Wang Xiaobin Xu Cunqun Fan Faculty of Information Technology Beijing University of Technology Beijing China Atmospheric Remote Sensing Satellite Data Center National Satellite Meteorological Center Beijing China

With the support of satellite edge computing, Internet of Vehicles (IoV) are utilized more and more widely, especially in ground network unreachable situations. To support more complex edge computing services, the aggregation of satellite computing resources becomes a possible solution. This paper models the aggregated resource management to be a non-cooperative two-stage Stackelberg game framework, and a distributed algorithm is proposed to speed up the convergence of resource allocation. Simulation results demonstrate that the proposed method can acquire maximum utilities of both ground vehicles and satellites.

关键词： Satellites Simulation Computational modeling Games Land vehicles Resource management distributed algorithms

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Equilibrium Seeking in Learning-Based Noncooperative Nash Games

Equilibrium Seeking in Learning-Based Noncooperative Nash Ga...

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IEEE Conference on Decision and Control

作者： Paolo Scarabaggio Nicola Mignoni Raffaele Carli Mariagrazia Dotoli

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Traditionally, based on convexity, multi-agent decision-making models can hardly handle scenarios where agents’ cost functions defy this assumption, which is specifically required to ensure the existence of several equilibrium concepts. More recently, the advent of machine learning (ML), with its inherent non-convexity, has changed the conventional approach of pursuing convexity at all costs. This paper explores and integrates the robustness of game theoretic frameworks in managing conflicts among agents with the capacity of ML approaches, such as deep neural networks (DNNs), to capture complex agent behaviors. Specifically, we employ feed-forward DNNs to characterize agents’ best response actions rather than modeling their goals with convex functions. We introduce a technical assumption on the weight of the DNN to establish the existence and uniqueness of Nash equilibria and present two distributed algorithms based on fixed-point iterations for their computation. Finally, we demonstrate the practical application of our framework to a noncooperative community of smart energy users under a dynamic time-of-use energy pricing scheme.

关键词： Couplings Computational modeling Artificial neural networks Games Pricing Machine learning Cost function Robustness distributed algorithms Game theory

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On the Inconsistency of Estimation with Uncertain Models

On the Inconsistency of Estimation with Uncertain Models

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Asilomar Conference on Signals, Systems & Computers

作者： César A. Uribe James Zachary Hare Lance M. Kaplan Ali Jadbabaie Rice University Houston TX USA DEVCOM Army Research Laboratory Adelphi MD USA Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

We study the hypothesis testing problem where an agent seeks to select a hypothesis from a finite set $\Theta$ , based on a sequence of i.i.d. observations of a random variable $X_{k}\sim P^{\ast}$ for $k\geq 1$ with unknown distribution $P^{\ast}$ . Each hypothesis $\theta^{\ast}\in\Theta$ states that $X_{k}\sim P_{\theta}$ . The objective is to find a hypothesis $\theta^{\ast}$ such that $\theta= \arg\min\nolimits_{\theta\in\Theta}D_{KL}(P^{\ast}\Vert P_{\theta})$ . However, the set of hypotheses $\{P_{\theta}\}$ is partially known, where only a finite number of observations are available for the random variables $Y_{s}^{\theta}\sim P_{\theta}$ with $\theta\in \Theta$ . We show that contrary to classical Bayesian approaches, the obtained estimator will not be consistent, and the aggregated log-likelihood ratios will converge in distribution to a Gaussian distribution even when $k\rightarrow\infty$ . Our result states that estimators with uncertain likelihoods will not concentrate on the true hypothesis. There is a strictly positive probability that the belief in a suboptimal hypothesis is maximal.

关键词： Computers Algorithm design and analysis Analytical models Computational modeling Estimation Gaussian distribution Random variables Bayes methods distributed algorithms Testing

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Quantized distributed Nonconvex Optimization with Linear Convergence

Quantized Distributed Nonconvex Optimization with Linear Con...

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IEEE Conference on Decision and Control

作者： Lei Xu Xinlei Yi Jiayue Sun Yang Shi Karl Henrik Johansson Tao Yang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Digital Futures Stockholm Sweden Department of Mechanical Engineering University of Victoria Victoria Canada

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper considers distributed nonconvex op-timization for minimizing the average of local cost functions, by using local information exchange over undirected communication networks. Since the communication channels often have limited bandwidth or capacity, we first introduce a quantization rule and an encoder/decoder scheme to reduce the transmission bits. By integrating them with a distributed algorithm, we then propose a distributed quantized nonconvex optimization algorithm. Assuming the global cost function satisfies the Polyak– Łojasiewicz condition, which does not require the global cost function to be convex and the global minimizer is not necessarily unique, we show that the proposed algorithm linearly converges to a global optimal point. Moreover, a low data rate is shown to be sufficient to ensure linear convergence when the algorithm parameters are properly chosen. The theoretical results are illustrated by numerical simulation examples.

关键词： Quantization (signal) Communication channels Bandwidth Cost function Numerical simulation Communication networks distributed algorithms

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What if Autonomous Systems Had a Game Master? Targeted Explaining With the Help of a Supervisory Control System

What if Autonomous Systems Had a Game Master? Targeted Expla...

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Explainability Engineering (ExEn), IEEE/ACM Workshop on

作者： Akhila Bairy Martin Fränzle Carl von Ossietzky Universität Oldenburg Oldenburg Germany

ISBN: (数字)9798400705960

ISBN: (纸本)9798350388701

In the era of increasing automation, systems are making more autonomous decisions than ever before, often leading to conflicts with other systems. In situations where humans are entangled in such conflicts without proper explanation, they experience frustration. The consideration of the human’s frustration level in such scenarios therefore is a key aspect of solving these conflicts. Our approach involves synthesizing an appropriate supervisory control system (SCS) within a gaming context. The main focus of this paper is to seamlessly blend a SCS with conflict resolution capabilities, capitalizing on the explanatory potential. The central objective is to explore how this integration can be accomplished by employing the SCS as a game master or as a moderator within a game-theoretic framework. In this capacity, the system becomes a central authority equipped with comprehensive information relevant to the game, possessing the knowledge and insights required to navigate intricate scenarios. Employing a game-theoretic approach allows us to harness the system’s in-depth understanding of the game’s dynamics to strategically resolve conflicts and optimize outcomes. The game master utilises an explanation model designed to provide understandable and situationally aware explanations, effectively mitigating frustration levels for humans involved in the *** CONCEPTS • Theory of computation→Algorithmic game theory; • Humancentered computing→User models; • Computing methodologies→ Modeling methodologies.

关键词： Navigation Computational modeling Scalability Games Supervisory control User experience Vehicle dynamics Game theory distributed algorithms Context modeling

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An Asynchronous Peer-to-Peer Energy Trading among Heterogeneous Low-Carbon Building Prosumers

An Asynchronous Peer-to-Peer Energy Trading among Heterogene...

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IEEE General Meeting Power& Energy Society

作者： Xiaoyu Wang Hongjie Jia Xiaolong Jin Yunfei Mu Wei Wei Xiaodan Yu Shuo Liang Wanxin Tang Key Laboratory of Smart Grid of Ministry of Education and the Key Laboratory of Smart Energy & Information Technology of Tianjin Municipality Tianjin University Tianjin China

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Low-carbon buildings (LCBs) are normally equipped with distributed energy resources (DERs), thereby creating LCB prosumers with capacities for energy production and consumption. Peer-to-Peer (P2P) energy trading among LCB prosumers could bring higher economic benefits for themselves. To fully harness the potential benefits of LCBs in P2P energy trading, an asynchronous P2P energy trading method among heterogeneous LCB prosumers is proposed in this paper. The flexibility of heating loads of LCBs is fully exploited to benefit LCB prosumers in P2P energy trading by using the thermal dynamics of LCBs. Additionally, each LCB prosumer is heterogeneous in terms of the energy resources configuration and communication network infrastructure, which causes the heavy computation and communication burden using the traditional solution method. To improve the computational efficiency, an asynchronous distributed algorithm based on alternating direction method of multipliers (ADMM) is introduced to enable each LCB prosumer to trade energy asynchronously with no need to wait for the trading information from other LCB prosumers with poor communication network infrastructure. This asynchronous procedure significantly reduces the computation time of P2P energy trading. Simulation results verify the effectiveness of the proposed method and the feasibility of the proposed algorithm.

关键词： Economics Simulation Buildings Water heating Convex functions Peer-to-peer computing Computational efficiency Communication networks Thermal loading distributed algorithms

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distributed $L_{2}-L_{\infty}$ Containment Control for Multi Agent System

Distributed $L_{2}-L_{\infty}$ Containment Control for Multi...

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Chinese Control Conference (CCC)

作者： Yangming Hu Chao Liu Shool of Automation Central South University Changsha P. R. China

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper, we investigate the distributed $L_{2}-L_{\infty}$ containment control problem of multi agent systems with a desired performance index. The objective is to use local information to make all followers stay in the convex hull formed by multiple stationary leaders while satisfying a desired $L_{2}-L_{\infty}$ performance index in the presence of external disturbances. Based on Lyapunov function, some sufficient conditions are given to solve containment control problem with the desired $L_{2}-L_{\infty}$ performance.

关键词： Sufficient conditions Delay effects Performance analysis Trajectory Topology distributed algorithms Lyapunov methods

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A linear-time self-stabilizing distributed algorithm for the minimal minus ($L, K, Z$) -domination problem under the distance-2 model

A linear-time self-stabilizing distributed algorithm for the...

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International Symposium on Computing and Networking Workshops (CANDARW)

作者： Hirotsugu Kakugawa Sayaka Kamei Ryukoku University Otsu Shiga Japan Hiroshima University Higashi Hiroshima Hiroshima Japan

The domination problem is one of the fundamental graph problems and there are many variations. The problem has practical applications in a distributed setting, and studied well in the distributed computing community. In this paper, we propose a new problem called the minus ( $L, K, Z$ ) -domination problem where $L, K$ , and $Z$ are integers such that $L\leq-1,K\geq 1$ , and $Z\geq 1$ . The minus ( $L, K, Z$ ) -domination problem is a problem to assign a value $L, L+1, \ldots, 0, \ldots, K-1, K$ for each vertex in a graph such that the local summation of values is greater than or equal to $Z$ . Because it is the same as the minus domination problem when $L=-1, K=1$ and $Z=1$ , it is an extension of the minus domination problem. Then, we propose a self-stabilizing distributed algorithm for the minus ( $L, K, Z$ ) -domination problem, where self-stabilization is a class of fault-tolerant distributed algorithms that tolerate arbitrary finite number of transient faults. The proposed algorithm is designed under the distance-2 model and the unfair central daemon, and its convergence time is $O(n)$ , that is, linear to $n$ , where $n$ is the number of processes. If it is converted into the ordinary distance-1 model with a transformer, we obtain an algorithm whose convergence time is $O(nm)$ .

关键词： Fault tolerance Conferences Computational modeling Fault tolerant systems Transformers distributed algorithms Time complexity

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A distributed Algorithm for Aggregative Games on Directed Communication Graphs

A Distributed Algorithm for Aggregative Games on Directed Co...

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IEEE Conference on Decision and Control

作者： Sina Arefizadeh Angelia Nedić School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This study focuses on aggregative games, a type of Nash games that is played over a network. In these games, the cost function of an agent is affected by its own choice and the sum of all decision variables of the players involved. We consider a distributed algorithm over a network, whereby to reach a Nash equilibrium point, each agent maintains a prediction of the aggregate decision variable and share it with its local neighbors over a strongly connected directed network. The existing literature provides such algorithms for undirected graphs which typically require the use doubly stochastic weight matrices. We consider a fixed directed communication network and investigate a synchronous distributed gradient-based method for computing a Nash equilibrium. We provide convergence analysis of the method showing that the algorithm converges to the Nash equilibrium of the game, under some standard conditions.

关键词： Aggregates Games Nash equilibrium Prediction algorithms Cost function Communication networks distributed algorithms

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