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30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

作者： Lu, Fred Curtin, Ryan R. Raff, Edward Ferraro, Francis Holt, James Booz Allen Hamilton Mclean VA 22102 USA Univ Maryland Baltimore MD USA Lab Phys Sci College Pk MD USA

ISBN: (纸本)9798400704901

As the size of datasets used in statistical learning continues to grow, distributed training of models has attracted increasing attention. These methods partition the data and exploit parallelism to reduce memory and runtime, but suffer increasingly from communication costs as the data size or the number of iterations grows. Recent work on linear models has shown that a surrogate likelihood can be optimized locally to iteratively improve on an initial solution in a communication-efficient manner. However, existing versions of these methods experience multiple shortcomings as the data size becomes massive, including diverging updates and efficiently handling sparsity. In this work we develop solutions to these problems which enable us to learn a communication-efficient distributed logistic regression model even beyond millions of features. In our experiments we demonstrate a large improvement in accuracy over distributed algorithms with only a few distributed update steps needed, and similar or faster runtimes. Our code is available at https://***/FutureComputing4AI/ProxCSL.

关键词： distributed algorithms communication-efficient surrogate likelihood sparse models

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Securely Solving Linear Algebraic Equations in a distributed Framework Enhanced With Communication-Efficient algorithms

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2020年第3期7卷 1027-1042页

作者： Yin, Bo Shen, Wenlong Cao, Xianghui Cheng, Yu Li, Qing IIT Dept Elect & Comp Engn Chicago IL 60616 USA Southeast Univ Sch Automat Nanjing 210096 Peoples R China Symantec Corp Mountain View CA 94043 USA

Solving linear algebraic equations (a.k.a., an LAE problem) distributedly in a network with multiple agents has wide applications in distributed control, estimation, and signal processing. A consensus-based distributed computing framework is studied in this paper. Specifically, each agent knows only a subproblem of the LAE, i.e., a subset of all equations, and then all agents apply a consensus-based algorithm to update their estimates of the correct solution of the LAE problem iteratively. Under certain conditions, it has been shown that all the estimates converge to the exact solution exponentially fast. However, such a distributed paradigm is vulnerable to malicious behaviors in an adversarial environment. In this paper, we indicate a number of security threats in this process, and thus develop robust computing solutions against those attacks. With particular attention to low storage overhead, we develop a new alternating projection method to enhance the consensus algorithm. Furthermore, we design an innovative misbehavior detection mechanism by exploiting the homomorphic signature technique. Our method can detect misbehaving agents without the common, yet sometimes infeasible, assumption of local good majority. Another significant contribution presented in this paper is an original component dropping approach for mitigating the communication overhead during the consensus process. From both theoretical and engineering perspectives, we study how the consensus can still be reached when a big portion of elements in exchanged message vectors are dropped. In our preliminary numerical results, roughly 80% data transmission can be reduced per epoch at the expense of 35% extra epochs to reach the consensus, implying 73% reduction in terms of the communication overhead.

关键词： Outsourcing distributed computing distributed algorithms Optimization Monitoring Privacy Security distributed consensus linear algebraic equations security

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Impact of grid partitioning algorithms on combined distributed AC optimal power flow and parallel dynamic power grid simulation

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第25期14卷 6133-6141页

作者： Kyesswa, Michael Murray, Alexander Schmurr, Philipp Cakmak, Hueseyin Kuehnapfel, Uwe Hagenmeyer, Veit Karlsruhe Inst Technol Inst Automat & Appl Informat Karlsruhe Germany

The complexity of most power grid simulation algorithms scales with the network size, which corresponds to the number of buses and branches in the grid. Parallel and distributed computing is one approach that can be used to achieve improved scalability. However, the efficiency of these algorithms requires an optimal grid partitioning strategy. To obtain the requisite power grid partitionings, the authors first apply several graph theory based partitioning algorithms, such as the Karlsruhe fast flow partitioner (KaFFPa), spectral clustering, and METIS. The goal of this study is an examination and evaluation of the impact of grid partitioning on power system problems. To this end, the computational performance of AC optimal power flow (OPF) and dynamic power grid simulation are tested. The partitioned OPF-problem is solved using the augmented Lagrangian based alternating direction inexact Newton method, whose solution is the basis for the initialisation step in the partitioned dynamic simulation problem. The computational performance of the partitioned systems in the implemented parallel and distributed algorithms is tested using various IEEE standard benchmark test networks. KaFFPa not only outperforms other partitioning algorithms for the AC OPF problem, but also for dynamic power grid simulation with respect to computational speed and scalability.

关键词： Newton method optimisation load flow IEEE standards power grids graph theory pattern clustering parallel algorithms power system simulation computational complexity optimal grid partitioning strategy spectral clustering power system problems computational performance partitioned OPF-problem augmented Lagrangian based alternating direction inexact Newton method partitioned dynamic simulation problem partitioned systems parallel algorithm distributed algorithms AC OPF problem grid partitioning algorithms combined distributed AC optimal power flow parallel dynamic power grid simulation distributed computing power grid simulation algorithms power grid partitionings parallel computing graph theory Karlsruhe fast flow partitioner KaFFPa METIS IEEE standard benchmark test networks

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Decentralized Interference-Aware Codebook Learning in Millimeter Wave MIMO Systems

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IEEE TRANSACTIONS ON COMMUNICATIONS 2025年第5期73卷 3608-3621页

作者： Zhang, Yu Alkhateeb, Ahmed Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85281 USA

Beam codebooks are integral components of future millimeter wave MIMO systems. Therefore, it is critical to optimize these codebooks for efficient and reliable communications. Prior work has focused on single-cell codebook learning problems and under stationary interference. In this work, we generalize the interference-aware codebook learning problem to networks with multiple cells/basestations. One of the key differences is that the underlying environment becomes non-stationary, as the behavior of one basestation may influence the learning of the others. Further, we avoid information exchange between different learning nodes which leads to a fully decentralized system with increased learning difficulties. To tackle the non-stationarity, the averaging of measurements is used to estimate the interference nulling performance of a particular beam, based on which a decision rule is provided. Furthermore, we theoretically justify the adoption of such estimator, and prove that it is a sufficient statistic for the underlying quantity of interest in an asymptotic sense. Finally, a novel reward function is proposed to decouple the learning of the multiple agents running at different nodes. Results show that the developed solution is capable of learning well-shaped codebook patterns for different networks and significantly suppress the interference without requiring any information exchange between basestations.

关键词： Interference Base stations Millimeter wave communication MIMO communication Vectors Information exchange Channel models Symbols Training Synchronization MIMO millimeter wave communication interference cancellation reinforcement learning distributed algorithms

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distributed Learning for Autonomous Vehicles in Communication-Constrained Environments 20

Distributed Learning for Autonomous Vehicles in Communicatio...

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IEEE 20th International Conference on Automation Science and Engineering (CASE)

作者： Cruz, Nicole Fuentes, Jose Bobadilla, Leonardo Florida Int Univ Sch Comp & Informat Sci Miami FL 33199 USA

ISBN: (纸本)9798350358513;9798350358520

Intelligent Autonomous Systems (IAS), which encompass Unmanned Aerial Systems (UAS), Unmanned Surface Vehicles (USV), and Unmanned Underwater Vehicles (UUV), are set to play a pivotal role across multiple domains. However, due to the demanding nature of their missions (e.g., disaster response and underwater exploration), common communication methods are often limited. This makes it essential to use efficient communication strategies for learning and decisionmaking. This paper introduces a novel approach, leveraging distributed machine learning algorithms along with data projection to achieve sublinear communication costs. Particularly, we incorporate Convolutional Neural Networks for feature extraction prior to projection, significantly enhancing efficiency and accuracy in autonomy tasks within bandwidth-constrained environments. This method uses the Johnson-Lindenstrauss transform for dimensionality reduction of both data and model parameters, facilitating efficient inter-agent communication by transmitting only projected model parameters. We further outline theoretical underpinnings showcasing the JohnsonLindenstrauss transform's effectiveness in maintaining convergence properties for Online Gradient Descent algorithms. Through experimental validation using image recognition tasks relevant to maritime surveillance, our approach demonstrates substantial reductions in communication cost while preserving high model performance.

关键词： distributed algorithms Convolutional neural networks Machine learning Maritime communication

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Deconfliction of Centralized and distributed ADMS and DERMS Applications

Deconfliction of Centralized and Distributed ADMS and DERMS ...

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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Anderson, Alexander Fisher, Andrew Poudel, Shiva Vasios, Orestis Black, Gary Reiman, Andrew Pacific Northwest National Laboratory RichlandWA United States

ISBN: (纸本)9798350352528

Ongoing distribution grid modernization efforts are introducing an increasing number of advanced applications granted control of field devices. An emerging challenge is the deconfliction problem of coordinating conflicting control commands issued by multiple applications to the same device. As an alternative to custom, centralized grid orchestration techniques, the concept of distributed deconfliction is introduced. Conflicts between centralized and distributed applications are decomposed based on definition of local distributed control areas based on the network topology. This work introduces a distributed deconfliction architecture derived from prior work in centralized deconfliction and distributed application architecture. Setpoint-informed optimization and objective-informed multi-criteria decision-making are compared as two possible numerical techniques for selection of final device setpoints. © 2025 IEEE.

关键词： common information model distributed algorithms distributed power generation edge computing power distribution control

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Input-Output Performance of Linear-Quadratic Saddle-Point algorithms With Application to distributed Resource Allocation Problems

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2020年第5期65卷 2032-2045页

作者： Simpson-Porco, John W. Poolla, Bala Kameshwar Monshizadeh, Nima Dorfler, Florian Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Swiss Fed Inst Technol Zurich Automat Control Lab CH-8092 Zurich Switzerland Univ Groningen Engn & Technol Inst NL-9747 AG Groningen Netherlands

Saddle-point or primal-dual methods have recently attracted renewed interest as a systematic technique to design distributed algorithms, which solve convex optimization problems. When implemented online for streaming data or as dynamic feedback controllers, these algorithms become subject to disturbances and noise;convergence rates provide incomplete performance information, and quantifying input-output performance becomes more important. We analyze the input-output performance of the continuous-time saddle-point method applied to linearly constrained quadratic programs, providing explicit expressions for the saddle-point $\mathcal {H}_2$ norm under a relevant input-output configuration. We then proceed to derive analogous results for regularized and augmented versions of the saddle-point algorithm. We observe some rather peculiar effects-a modest amount of regularization significantly improves the transient performance, while augmentation does not necessarily offer improvement. We then propose a distributed dual version of the algorithm, which overcomes some of the performance limitations imposed by augmentation. Finally, we apply our results to a resource allocation problem to compare the input-output performance of various centralized and distributed saddle-point implementations and show that distributed algorithms may perform as well as their centralized counterparts.

关键词： Optimization Heuristic algorithms Convergence Resource management Standards distributed algorithms Transient analysis distributed algorithms input-output performance primal-dual dynamics resource allocation saddle-point methods system norms

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Near-optimal dispersion on arbitrary anonymous graphs

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JOURNAL OF COMPUTER AND SYSTEM SCIENCES 2025年 152卷

作者： Kshemkalyani, Ajay D. Sharma, Gokarna Univ Illinois Dept Comp Sci Chicago IL USA Kent State Univ Dept Comp Sci Kent OH 44240 USA

Given an undirected, anonymous, port-labeled graph of n memory-less nodes, m edges, and degree Delta, we consider the problem of dispersing k <= n robots (or tokens) positioned initially arbitrarily on the nodes of the graph to exactly k different nodes, one on each node. The objective is to simultaneously minimize time and memory requirement at each robot. The best previously known algorithm solves this problem in O(min{m,k Delta}.log l) time storing O(log(k+Delta)) bits at each robot, where l <= k/2 is the number of nodes with multiple robots positioned on them in the initial configuration. In this paper, we present a novel multi-source DFS traversal algorithm solving this problem in O(min{m,k Delta}) time with O(log(k+Delta)) bits at each robot. The memory complexity of our algorithm is already asymptotically optimal and the time complexity is asymptotically optimal for the graphs of constant degree Delta=O(1). The result holds in both synchronous and asynchronous settings. (c) 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC license (http://***/licenses/by-nc/4.0/).

关键词： distributed algorithms Multi-agent systems Mobile robots Local communication Dispersion Exploration Time and memory complexity

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Adjacent Channel Interference and Congestion Control for Multi-Channel Operation in Vehicular Networks

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第5期26卷 6661-6674页

作者： Egea-Lopez, Esteban Pavon-Marino, Pablo Univ Politcn Cartagena UPCT Dept Informat & Commun Technol Cartagena 30202 Spain

ETSI has considered a new set of services for the Release 2, which cannot be implemented using the single control channel. Therefore, it is necessary to regulate the operation of applications on multiple channels, what is called Multi-Channel Operation (MCO). In this case the interference from the first adjacent channel is not negligible and is recommended to manage it by congestion control mechanisms. We propose an elastic channel usage scheme for MCO, based on an optimal scheduler whose goal is to maximize the traffic on the channels while minimizing the adjacent channel interference generated by that load and controlling the congestion. We formulate the interference and congestion control as a constrained convex optimization problem and derive as solution a distributed algorithm called MINOS (MultI-chaNnel operation Optimal Scheduler). MINOS seamlessly work when vehicles have a different number of network interfaces available, and constraints and priorities can be set individually and dynamically, which provides flexibility to implement more sophisticated services on top of the framework. Our results show that MINOS effectively controls the congestion and reduces the interference, achieving an increased packet reception ratio across all channels and a higher allocated traffic, compared to other proposals.

关键词： Interference Proposals Radio access technologies Interchannel interference Optimal scheduling Network interfaces distributed algorithms Load modeling Intelligent transportation systems Filling Vehicular communications congestion control scheduling multi-channel operation MCO adjacent interference

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Exact distributed Load Centrality Computation: algorithms, Convergence, and Applications to Distance Vector Routing

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IEEE TRANSACTIONS ON PARALLEL AND distributed SYSTEMS 2020年第7期31卷 1693-1706页

作者： Maccari, Leonardo Ghiro, Lorenzo Guerrieri, Alessio Montresor, Alberto Lo Cigno, Renato Univ Venice I-30123 Venice Italy Univ Trento I-38122 Trento Italy SpazioDati Srl I-56021 Cascina Italy Univ Brescia I-25121 Brescia Italy

Many optimization techniques for networking protocols take advantage of topological information to improve performance. Often, the topological information at the core of these techniques is a centrality metric such as the Betweenness Centrality (BC) index. BC is, in fact, a centrality metric with many well-known successful applications documented in the literature, from resource allocation to routing. To compute BC, however, each node must run a centralized algorithm and needs to have the global topological knowledge;such requirements limit the feasibility of optimization procedures based on BC. To overcome restrictions of this kind, we present a novel distributed algorithm that requires only local information to compute an alternative similar metric, called Load Centrality (LC). We present the new algorithm together with a proof of its convergence and the analysis of its time complexity. The proposed algorithm is general enough to be integrated with any distance vector (DV) routing protocol. In support of this claim, we provide an implementation on top of Babel, a real-world DV protocol. We use this implementation in an emulation framework to show how LC can be exploited to reduce Babel's convergence time upon node failure, without increasing control overhead. As a key step towards the adoption of centrality-based optimization for routing, we study how the algorithm can be incrementally introduced in a network running a DV routing protocol. We show that even when only a small fraction of nodes participate in the protocol, the algorithm accurately ranks nodes according to their centrality.

关键词： Routing protocols Measurement Routing Indexes distributed algorithms Network topology Multi-hop networks mesh networks ad-hoc networks bellman-ford load centrality distributed algorithms failure recovery

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