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Differentially Private Nash Equilibrium Seeking in Quadratic Network Games

IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2025年第1期12卷 673-686页

作者： Wang, Lei Ding, Kemi Leng, Yan Ren, Xiaoqiang Shi, Guodong Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China Southern Univ Sci & Technol Shenzhen Key Lab Control Theory & Intelligent Syst Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Sch Syst Design & Intelligent Mfg Shenzhen 518055 Peoples R China Univ Texas Austin McCombs Sch Business Austin TX 78735 USA Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Minist Educ Key Lab Marine Intelligent Unmanned Swarm Technol Shanghai 200444 Peoples R China Univ Sydney Australian Ctr Robot Sydney NSW 2006 Australia

In this article, we develop distributed computation algorithms for Nash equilibriums of linear-quadratic network games with proven differential privacy guarantees. In a network game with each player's payoff being a quadratic function, the dependencies of the decisions in the payoff function naturally encode a network structure governing the players' interpersonal influences. Such social influence structure and the individual marginal payoffs of the players indicate economic spillovers and individual preferences, and thus, they are subject to privacy concerns. For distributed computing of the Nash equilibrium, the players are interconnected by a public communication graph, over which dynamical states are shared among neighboring nodes. When the players' marginal payoffs are considered to be private knowledge, we propose a distributed randomized gradient descent algorithm, in which each player adds a Laplacian random noise to her marginal payoff in the recursive updates. It is proven that the algorithm can guarantee differential privacy and convergence in expectation to the Nash equilibrium of the network game at each player's state. Moreover, the mean-square error between the players' states and the Nash equilibrium is shown to be bounded by a constant related to the differential privacy level. Next, when both the players' marginal payoffs and the influence graph are private information, we propose two distributed algorithms by randomized communication and randomized projection, respectively, for privacy preservation. The differential privacy and convergence guarantees are also established for such algorithms.

关键词： Differential privacy distributed computation linear-quadratic network game

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Trustworthy distributed Certification of Program Execution

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2025年第4期51卷 1134-1152页

作者： Wolf, Alex Palma, Marco Edoardo Salza, Pasquale Gall, Harald C. Univ Zurich CH-8006 Zurich Switzerland

Verifying the execution of a program is complicated and often limited by the inability to validate the code's correctness. It is a crucial aspect of scientific research, where it is needed to ensure the reproducibility and validity of experimental results. Similarly, in customer software testing, it is difficult for customers to verify that their specific program version was tested or executed at all. Existing state-of-the-art solutions, such as hardware-based approaches, constraint solvers, and verifiable computation systems, do not provide definitive proof of execution, which hinders reliable testing and analysis of program results. In this paper, we propose an innovative approach that combines a prototype programming language called Mona with a certification protocol OCCP to enable the distributed and decentralized re-execution of program segments. Our protocol allows for certification of program segments in a distributed, immutable, and trustworthy system without the need for naive re-execution, resulting in significant improvements in terms of time and computational resources used. We also explore the use of blockchain technology to manage the protocol workflow following other approaches in this space. Our approach offers a promising solution to the challenges of program execution verification and opens up opportunities for further research and development in this area. Our findings demonstrate the efficiency of our approach in reducing the number of program executions by up to 20-fold, while maintaining resilience against various malicious attacks compared to existing state-of-the-art methods, thus improving the efficiency of certifying program executions. Additionally, our approach handles up to 40% malicious workers effectively, showcasing resilience in detecting and mitigating malicious behavior. In the Equivalent Registers Attack scenario, it successfully identifies divergent executions even when register values and results appear identical. Moreover, our

关键词： Logic Codes Protocols Data mining Artificial intelligence Software Certification Blockchains Annotations Hardware Program execution certification distributed computation blockchain

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distributed Multi-Pair computation for Intra C-RAN Bidirectional Communications

Distributed Multi-Pair Computation for Intra C-RAN Bidirecti...

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Biennial Symposium on Communications (BSC)

作者： Hasabelnaby, Mahmoud A. Chaaban, Anas Univ British Columbia Sch Engn Kelowna BC V1V 1V7 Canada

ISBN: (纸本)9781665493802

Cloud radio-access network (C-RAN) performance is limited by fronthaul capacity constraints. To alleviate this limitation, distributed processing between the distributed radio units (RUs) and the central processing unit (CPU) can be used to relax the communication overhead over the fronthaul links. In this paper, a distributed multi-pair computation approach is proposed for a C-RAN with intra-cloud bidirectional communication, wherein some user-pairs are served locally by an RU whereas others are served centrally at the CPU. The achievable end-to-end rate is derived, optimized, and numerically evaluated under limited fronthaul capacity and power constraints. Numerical evaluations reveal the proposed scheme can overcome the fronthaul capacity limitation and significantly improve the achievable end-to-end sum-rate compared to conventional centralized ones.

关键词： C-RAN Intra-cloud communications distributed computation Two-way multi-pair processing

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distributed Grobner bases computation with MPJ

Distributed Grobner bases computation with MPJ

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IEEE 27th International Conference on Advanced Information Networking and Applications Workshops (WAINA)

作者： Kredel, Heinz Univ Mannheim IT Ctr Mannheim Germany

ISBN: (纸本)9780769549521;9781467362399

Grobner bases, an important building block in computer algebra, are computed on distributed memory parallel computers with multi-CPU compute nodes with increasing success. Based on previous efforts we present and compare new programs using the MPI message passing standard implemented using MPJ. MPJ is a specification of MPI for Java with efficient implementations. Also the new versions use one control communication channel between the master node and the worker nodes. In the hybrid version the polynomials are shared between the threads on a node and in the pure distributed version the polynomials are replicated on every process. The polynomials are transported asynchronous to the control-flow of the algorithm in a separate distributed data structure. The implementation is generic and works for all implemented fields. We present first promising performance measurements.

关键词： computer algebra distributed computation Groebner bases MPI MPJ

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Coded computation for Speeding Up distributed Machine Learning

Coded Computation for Speeding Up Distributed Machine Learni...

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作者： Wang, Sinong The Ohio State University

学位级别：Ph.D.

Large-scale machine learning has shown great promise for solving many practical applications. Such applications require massive training datasets and model parameters, and force practitioners to adopt distributed computing frameworks such as Hadoop and Spark to increase the learning speed. However, the speedup gain is far from ideal due to the latency incurred in waiting for a few slow or faulty processors, called "straggler" to complete their tasks. To alleviate this problem, current frameworks such as Hadoop deploy various straggler detection techniques and usually replicate the straggling task on another available node, which creates a large computation overhead. In this dissertation, we focus on a new and more effective technique, called coded computation to deal with stragglers in the distributed computation problems. It creates and exploits coding redundancy in local computation to enable the final output to be recoverable from the results of partially finished workers, and can therefore alleviate the impact of straggling workers. However, we observe that current coded computation techniques are not suitable for large-scale machine learning application. The reason is that the input training data exhibit both extremely large-scale targeting data and a sparse structure. However, the existing coded computation schemes destroy the sparsity and creates large computation redundancy. Thus, while these schemes reduce delays due to the stragglers in the system, they create additional delays because they end up increasing the computational load on each machine. This fact motivates us to focus on designing more efficient coded computation scheme for machine learning applications. We begin by investigating the linear transformation problem. We analyze the minimum computation load (number of redundant computations in each worker) of any coded computation scheme for this problem, and construct a code we name "diagonal code" that achieves the above minimum computation load.

关键词： Computer Science Electrical Engineering Machine learning distributed computation information theory coding theory distributed optimization

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A distributed Randomized Approach for the PageRank computation: Part 2

A Distributed Randomized Approach for the PageRank Computati...

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47th IEEE Conference on Decision and Control

作者： Ishii, Hideaki Tempo, Roberto Tokyo Inst Technol Dept Computat Intelligence & Syst Sci Yokohama Kanagawa 2268502 Japan Politecn Torino CNR IEIIT I-10129 Turin Italy

ISBN: (纸本)9781424431243

In the search engine of Google, the PageRank algorithm plays a crucial role in ranking the obtained results. The algorithm determines the importance of each web page based on the link structure of the web. In this two-part paper, we propose a distributed randomized approach for the PageRank computation, where the pages locally update their values by communicating with linked pages. This paper is the second part, and we develop two enhanced distributed schemes which deal with simultaneous updates and update termination of the computations, respectively.

关键词： distributed computation Multi-agent consensus PageRank algorithm Randomization Stochastic matrices

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A New Approach for Aggregated PageRank computation via distributed Randomized Algorithms

A New Approach for Aggregated PageRank Computation via Distr...

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50th IEEE Conference of Decision and Control (CDC)/European Control Conference (ECC)

作者： Ishii, Hideaki Tempo, Roberto Bai, Er-Wei Tokyo Inst Technol Dept Computat Intelligence & Syst Sci Midori Ku 4259 Nagatsuta Cho Yokohama Kanagawa 2268502 Japan Politecn Torino IEIIT CNR I-10129 Turin Italy Univ Iowa Ctr Engn Arts & Sci Iowa City IA 52242 USA

ISBN: (纸本)9781612848013

At Google, the PageRank algorithm helps rankings in search results by providing measures of web page importance. This paper builds upon the distributed randomized approach for this algorithm proposed in our recent works. To reduce computation and communication, we develop a method to systematically aggregate web pages into groups by exploiting the sparsity inherent in the web. Each group computes an aggregated PageRank, which can be distributed among group members. We provide a decentralized scheme for its computation and analyze convergence properties.

关键词： distributed computation Multi-agent consensus PageRank algorithm Randomization Search engines

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computation Scheduling for distributed Machine Learning with Straggling Workers 44

Computation Scheduling for Distributed Machine Learning with...

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Amiri, Mohammad Mohammadi Gunduz, Deniz Imperial Coll London Elect & Elect Engn Dept London SW7 2BT England

ISBN: (纸本)9781479981311

We study scheduling of computation tasks across n workers in a large scale distributed learning problem. computation speeds of the workers are assumed to be heterogeneous and unknown to the master, and redundant computations are assigned to the workers in order to tolerate straggling workers. We consider sequential computation and instantaneous communication from each worker to the master, and each computation round, which can model a single iteration of the stochastic gradient descent (SGD) algorithm, is completed once the master receives k <= n distinct computations, referred to as the computation target. Our goal is to characterize the average completion time as a function of the computation load, which denotes the portion of the dataset available at each worker, and the computation target. We propose two computation scheduling schemes that specify the computation tasks assigned to each worker, as well as their order of execution. We also establish a lower bound on the minimum average completion time. Numerical results show a significant reduction in the average computation time over the existing coded and uncoded computing schemes.

关键词： Machine learning distributed computation

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A distributed Randomized Approach for the PageRank computation: Part 1

A Distributed Randomized Approach for the PageRank Computati...

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47th IEEE Conference on Decision and Control

作者： Ishii, Hideaki Tempo, Roberto Tokyo Inst Technol Dept Computat Intelligence & Syst Sci Yokohama Kanagawa 2268502 Japan Politecn Torino CNR IEIIT I-10129 Turin Italy

ISBN: (纸本)9781424431243

In the search engine of Google, the PageRank algorithm plays a crucial role in ranking the obtained results. The algorithm quantifies the importance of each web page based on the link structure of the web. In this two-part paper, we first provide an overview of the original problem setup. Then, we propose several distributed randomized schemes for the computation of the PageRank, where the pages can locally update their values by communicating to those connected by links. A detailed discussion on the close relations to the multi-agent consensus problems is also given.

关键词： distributed computation Multi-agent consensus PageRank algorithm Randomization Stochastic matrices

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distributed computational model for shared processing on Cyber-Physical System environments

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COMPUTER COMMUNICATIONS 2017年 111卷 68-83页

作者： Mora, Higinio Colom, Jose Francisco Gil, David Jimeno-Morenilla, Antonio Univ Alicante Specialized Processor Architecture Lab Dept Comp Sci Technol & Computat Alicante 03690 Spain Univ Alicante Dept Comp Sci Technol & Computat Lucentia Res Grp Alicante 03690 Spain Univ Alicante Dept Comp Sci Technol & Computat UniCAD Res Grp Alicante 03690 Spain

Cyber-Physical Systems typically consist of a combination of mobile devices, embedded systems and computers to monitor, sense, and actuate with the surrounding real world. These computing elements are usually wireless, interconnected to share data and interact with each other, with the server part and also with cloud computing services. In such a heterogeneous environment, new applications arise to meet ever-increasing needs and these are an important challenge to the processing capabilities of devices. For example, automatic driving systems, manufacturing environments, smart city management, etc. To meet the requirements of said application contexts, the system can create computing processes to distribute the workload over the network and/or a cloud computing server. Multiple options arise in relation to what network nodes should support the execution of the processes. This paper focuses on this problem by introducing a distributed computational model to dynamically share these tasks among the computing nodes and considering the inherent variability of the context in these environments. Our novel approach promotes the integration of the computing resources, with externally supplied cloud services, to fulfill modern application requirements. A prototype implementation for the proposed model has been built and an application example has been designed to validate the proposal in a real working environment. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Cyber-physical systems Internet of things Mobile computing Modeling distributed computation

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