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Distributed Constrained Online Nonconvex Optimization with Compressed Communication

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arXiv 2025年

作者： Zhang, Kunpeng Xu, Lei Yi, Xinlei Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai201210 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online nonconvex optimization with time-varying inequality constraints over a network of agents. For a time-varying graph, we propose a distributed online primal–dual algorithm with compressed communication to efficiently utilize communication resources. We show that the proposed algorithm establishes an O(T max{1−θ1,θ1}) network regret bound and an O(T 1−θ1/2) network cumulative constraint violation bound, where T is the number of iterations and θ1 ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints at all iterations), the network cumulative constraint violation bound is reduced to O(T 1−θ1). These bounds are comparable to the state-of-the-art results established by existing distributed online algorithms with perfect communication for distributed online convex optimization with (time-varying) inequality constraints. Finally, a simulation example is presented to validate the theoretical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

Data-driven Optimal Cooperative Output Regulation of Unknown Linear Discrete-time Multi-agent systems

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Data-driven Optimal Cooperative Output Regulation of Unknown...

Chinese control and decision Conference, CCDC

作者： Dong Liang Yi Dong Chaoli Wang Engang Tian Department of Control Science and Engineering School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China

The cooperative output regulation problem has been extensively studied on the basis of the distributed observer approach. However, the majority of the existing research assumes that the dynamics is known previously. To remove this condition, the cooperative output regulation problem is further solved via the data-driven framework where the dynamics of the plant is unknown. First, a data-driven distributed observer is established to estimate the state of the leader with unknown dynamics subject to external inputs. Second, the unknown regulator equations are solved using the iterative recurrent neural network approach. Third, the state-based data-driven distributed control law is synthesized to solve the problem. The optimal gains are derived by solving convex optimization problems using input and state data. Finally, a numerical example is presented to verify the feasibility of the proposed framework.

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Verifying Nonlinear Neural Feedback systems using Polyhedral Enclosures

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arXiv 2025年

作者： Akinwande, Samuel I. Sidrane, Chelsea Kochenderfer, Mykel J. Barrett, Clark Department of Aeronautical and Astronautical Engineering Stanford University StanfordCA94305 United States Department of Computer Science Stanford University StanfordCA94305 United States Division of Robotics Perception and Learning Intelligent Systems Department School of Electrical Engineering & Computer Science KTH Royal Institute of Technology Stockholm Sweden

As dynamical systems equipped with neural network controllers (neural feedback systems) become increasingly prevalent, it is critical to develop methods to ensure their safe operation. Verifying safety requires extending control theoretic analysis methods to these systems. Although existing techniques can efficiently handle linear neural feedback systems, relatively few scalable methods address the nonlinear case. We propose a novel algorithm for forward reachability analysis of nonlinear neural feedback systems. The approach leverages the structure of the nonlinear transition functions of the systems to compute tight polyhedral enclosures (i.e., abstractions). These enclosures, combined with the neural controller, are then encoded as a mixed-integer linear program (MILP). Optimizing this MILP yields a sound over-approximation of the forward-reachable set. We evaluate our algorithm on representative benchmarks and demonstrate an order of magnitude improvement over the current state of the art. Copyright © 2025, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

A New Family of Feasible Methods for Distributed Resource Allocation

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A New Family of Feasible Methods for Distributed Resource Al...

IEEE Conference on decision and control

作者： Xuyang Wu Sindri Magnússon Mikael Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Stockholm University Stockholm Sweden

ISBN: (纸本)9781665436601

Distributed resource allocation is a central task in network systems such as smart grids, water distribution networks, and urban transportation systems. When solving such problems in practice it is often important to have non-asymptotic feasibility guarantees for the iterates, since overall-location of resources easily causes systems to break down. In this paper, we develop a distributed resource reallocation algorithm where every iteration produces a feasible allocation. The algorithm is fully distributed in the sense that nodes communicate only with neighbors over a given communication network. We prove that under mild conditions the algorithm converges to a point arbitrarily close to the optimal resource allocation. Numerical experiments demonstrate the competitive practical performance of the algorithm.

关键词： Privacy Transportation Distribution networks Linear programming Smart grids Resource management Task analysis

A Sample-Based Algorithm for Approximately Testing r-Robustness of a Digraph

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arXiv 2022年

作者： Yi, Yuhao Wang, Yuan He, Xingkang Patterson, Stacy Johansson, Karl H. The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures StockholmSE-100 44 Sweden The Department of Electrical Engineering University of Notre Dame Notre Dame IN46556 United States The Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States

One of the intensely studied concepts of network robustness is r-robustness, which is a network topology property quantified by an integer r. It is required by mean subsequence reduced (MSR) algorithms and their variants to achieve resilient consensus. However, determining r-robustness is intractable for large networks. In this paper, we propose a sample-based algorithm to approximately test r-robustness of a digraph with n vertices and m edges. For a digraph with a moderate assumption on the minimum in-degree, and an error parameter 0 Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

Cloud-Fog Automation: The New Paradigm towards Autonomous Industrial Cyber-Physical systems

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arXiv 2025年

作者： Jin, Jiong Pang, Zhibo Kua, Jonathan Zhu, Quanyan Johansson, Karl H. Marchenko, Nikolaj Cavalcanti, Dave School of Engineering Swinburne University of Technology HawthornVIC3122 Australia Department of Intelligent Systems KTH Royal Institute of Technology Stockholm10044 Sweden Department of Automation Technology ABB Corporate Research Sweden Västerås72226 Sweden School of Information Technology Deakin University GeelongVIC3220 Australia Department of Electrical and Computer Engineering Tandon School of Engineering New York University BrooklynNY11201 United States Division of Decision and Control Systems School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm10044 Sweden Corporate Research Robert Bosch GmbH Renningen71272 Germany Intel Corporation Santa ClaraCA95054 United States

Autonomous Industrial Cyber-Physical systems (ICPS) represent a future vision where industrial systems achieve full autonomy, integrating physical processes seamlessly with communication, computing and control technologies while holistically embedding intelligence. Cloud-Fog Automation is a new digitalized industrial automation reference architecture that has been recently proposed. This architecture is a fundamental paradigm shift from the traditional International Society of Automation (ISA)-95 model to accelerate the convergence and synergy of communication, computing, and control towards a fully autonomous ICPS. With the deployment of new wireless technologies to enable almost-deterministic ultra-reliable low-latency communications, a joint design of optimal control and computing has become increasingly important in modern ICPS. It is also imperative that system-wide cyber-physical security are critically enforced. Despite recent advancements in the field, there are still significant research gaps and open technical challenges. Therefore, a deliberate rethink in co-designing and synergizing communications, computing, and control (which we term "3C co-design") is required. In this paper, we position Cloud-Fog Automation with 3C co-design as the new paradigm to realize the vision of autonomous ICPS. We articulate the state-of-the-art and future directions in the field, and specifically discuss how goal-oriented communication, virtualization-empowered computing, and Quality of Service (QoS)-aware control can drive Cloud-Fog Automation towards a fully autonomous ICPS, while accounting for system-wide cyber-physical security. © 2025, CC BY-SA.

关键词： computer control

Optimal querying for communication-efficient ADMM using Gaussian process regression

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Franklin Open

Franklin Open 2024年 6卷

作者： Duarte, Aldo Nghiem, Truong X. Wei, Shuangqing Division of Electrical and Computer Engineering School of Electrical Engineering and Commputer Science Louisiana State University Baton Rouge 70803 LA United States School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff 86011 AZ United States

In distributed optimization schemes consisting of a group of agents connected to a central coordinator, the optimization algorithm often involves the agents solving private local sub-problems and exchanging data frequently with the coordinator to solve the global distributed problem. In those cases, the query-response mechanism usually causes excessive communication costs to the system, necessitating communication reduction in scenarios where communication is costly. Integrating Gaussian processes (GP) as a learning component to the Alternating Direction Method of Multipliers (ADMM) has proven effective in learning each agent's local proximal operator to reduce the required communication exchange. A key element for integrating GP into the ADMM algorithm is the querying mechanism upon which the coordinator decides when communication with an agent is required. In this paper, we formulate a general querying decision framework as an optimization problem that balances reducing the communication cost and decreasing the prediction error. Under this framework, we propose a joint query strategy that takes into account the joint statistics of the query and ADMM variables and the total communication cost of all agents in the presence of uncertainty caused by the GP regression. In addition, we derive three different decision mechanisms that simplify the general framework by making the communication decision for each agent individually. We integrate multiple measures to quantify the trade-off between the communication cost reduction and the optimization solution's accuracy/optimality. The proposed methods can achieve significant communication reduction and good optimization solution accuracy for distributed optimization, as demonstrated by extensive simulations of a distributed sharing problem. © 2024 The Authors

关键词： ADMM Communication reduction Distributed optimization Gaussian process Proximal operator

Gradient Compression and Correlation Driven Federated Learning for Wireless Traffic Prediction

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Zhang, Chuanting Zhang, Haixia Dang, Shuping Shihada, Basem Alouini, Mohamed-Slim Shandong University School of Software Jinan250101 China Shandong University School of Control Science and Engineering Jinan250061 China Shandong University Shandong Key Laboratory of Wireless Communication Technologies Jinan250061 China University of Bristol Department of Electrical and Electronic Engineering BristolBS8 1UB United Kingdom Computer Electrical and Mathematical Science and Engineering Division Thuwal23955-6900 Saudi Arabia

Wireless traffic prediction plays an indispensable role in cellular networks to achieve proactive adaptation for communication systems. Along this line, Federated Learning (FL)-based wireless traffic prediction at the edge attracts enormous attention because of the exemption from raw data transmission and enhanced privacy protection. However FL-based wireless traffic prediction methods still rely on heavy data transmissions between local clients and the server for local model updates. Besides, how to model the spatial dependencies of local clients under the framework of FL remains uncertain. To tackle this, we propose an innovative FL algorithm that employs gradient compression and correlation-driven techniques, effectively minimizing data transmission load while preserving prediction accuracy. Our approach begins with the introduction of gradient sparsification in wireless traffic prediction, allowing for significant data compression during model training. We then implement error feedback and gradient tracking methods to mitigate any performance degradation resulting from this compression. Moreover, we develop three tailored model aggregation strategies anchored in gradient correlation, enabling the capture of spatial dependencies across diverse clients. Experiments have been done with two real-world datasets and the results demonstrate that by capturing the spatio-temporal characteristics and correlation among local clients, the proposed algorithm outperforms the state-of-the-art algorithms and can increase the communication efficiency by up to two orders of magnitude without losing prediction accuracy. © 2015 IEEE.

关键词： Prediction models

Optimal Linear Deception Attacks on Remote State Estimation with Constrained Alarm Rates: A Low-Dimensional Case

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Optimal Linear Deception Attacks on Remote State Estimation ...

IEEE Conference on decision and control

作者： Jun Shang Hanwen Zhang Jing Zhou Tongwen Chen Department of Control Science and Engineering Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems and Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack’s stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback–Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples.

关键词： Wireless communication Measurement Technological innovation System performance Kalman filters State estimation Intelligent sensors