检索结果-内蒙古大学图书馆

Privacy-Preserving Average consensus via State Decomposition

学校读者我要写书评

暂无评论

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2019年第11期64卷 4711-4716页

作者： Wang, Yongqiang Clemson Univ Dept Elect & Comp Engn Clemson SC 29634 USA

Average consensus underpins key functionalities of distributed systems ranging from distributed information fusion, decision-making, distributed optimization, to load balancing and decentralized control. Existing distributed average consensus algorithms require each node to exchange and disclose state information to its neighbors, which is undesirable in cases where the state is private or contains sensitive information. In this paper, we propose a novel approach that avoids disclosing individual state information in average consensus by letting each node decompose its state into 2 substates. For each node, one of the two substates involves in computation and internode interactions as if it were the original state, while the other substate interacts only with the first substate of the same node, being completely invisible to other nodes. The initial values of the two substates are chosen randomly but with their mean fixed to the initial value of the original state, which is key to guarantee convergence to the desired consensus value. In direct contrast to differential-privacy based privacy-preserving average-consensus approaches, which enable privacy by compromising accuracy in the consensus value, the proposed approach can guarantee convergence to the exact desired value without any error. Not only is the proposed approach able to prevent the disclosure of a nodes initial state to honest-but-curious neighbors, it can also provide protection against inference by external eavesdroppers able to wiretap communication links. Numerical simulations demonstrate the effectiveness of the approach and its advantages over state-of-the-art counterparts.

关键词： Privacy Convergence Cryptography Observability Couplings Optimization consensus algorithm Average consensus decomposition privacy preservation

T-S fuzzy model-based adaptive repetitive consensus control for multi-agent systems with imprecise communication topology structure

学校读者我要写书评

暂无评论

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2019年第8期50卷 1568-1579页

作者： Chen, Jiaxi Li, Junmin Zhao, Wenjie Xidian Univ Sch Math & Stat Xian 710071 Shaanxi Peoples R China

This paper studies the consensus problem of multi-agent systems (MAS) with imprecise communication topology structure (ICTS). T-S fuzzy model is used to express the ICTS. Through repeated learning techniques, this paper designs a distributed learning protocol that enables all agents reach consensus with periodic uncertainty parameters. The periodic uncertainty parameters are compensated based on a repetitive learning design method. With the information of leader agent is known to a small portion of following agents, an auxiliary control term is presented for each follower agent to handle leader's dynamic. Under the condition that the ICTS is fuzzy union connected, the learning control protocol proposed in this paper makes all the agents reach an agreement. In addition, the proposed consensus learning protocol is further promoted to solve the formation control problem. Sufficient conditions are given for the consensus and formation problems of the MAS by constructing a composite energy function, respectively. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed control protocol.

关键词： Adaptive control consensus algorithm MAS T-S fuzzy model formation control ICTS

Normalised Clustering Accuracy: An Asymmetric External Cluster Validity Measure

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Gagolewski, Marek Faculty of Mathematics and Information Science Warsaw University of Technology ul. Koszykowa 75 Warsaw00-662 Poland Systems Research Institute Polish Academy of Sciences ul. Newelska 6 Warsaw01-447 Poland

There is no, nor will there ever be, single best clustering algorithm. Nevertheless, we would still like to be able to distinguish between methods that work well on certain task types and those that systematically underperform. Clustering algorithms are traditionally evaluated using either internal or external validity measures. Internal measures quantify different aspects of the obtained partitions, e.g., the average degree of cluster compactness or point separability. However, their validity is questionable because the clusterings they endorse can sometimes be meaningless. External measures, on the other hand, compare the algorithms’ outputs to fixed ground truth groupings provided by experts. In this paper, we argue that the commonly used classical partition similarity scores, such as the normalised mutual information, Fowlkes–Mallows, or adjusted Rand index, miss some desirable properties. In particular, they do not identify worst-case scenarios correctly, nor are they easily interpretable. As a consequence, the evaluation of clustering algorithms on diverse benchmark datasets can be difficult. To remedy these issues, we propose and analyse a new measure: a version of the optimal set-matching accuracy, which is normalised, monotonic with respect to some similarity relation, scale-invariant, and corrected for the imbalancedness of cluster sizes (but neither symmetric nor adjusted for chance). © 2022, CC BY.

关键词： consensus algorithm

Robust Sparse Mean Estimation via Sum of Squares

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Diakonikolas, Ilias Kane, Daniel M. Karmalkar, Sushrut Pensia, Ankit Pittas, Thanasis University of Wisconsin-Madison United States University of California San Diego United States

We study the problem of high-dimensional sparse mean estimation in the presence of an ϵ-fraction of adversarial outliers. Prior work obtained sample and computationally efficient algorithms for this task for identity-covariance subgaussian distributions. In this work, we develop the first efficient algorithms for robust sparse mean estimation without a priori knowledge of the covariance. For distributions on d with "certifiably bounded" t-th moments and sufficiently light tails, our algorithm achieves error of O(ϵ1−1/t) with sample complexity m = (klog(d))O(t)/ϵ2−2/t. For the special case of the Gaussian distribution, our algorithm achieves near-optimal error of Õ(ϵ) with sample complexity m = O(k4polylog(d))/ϵ2. Our algorithms follow the Sum-of-Squares based, proofs to algorithms approach. We complement our upper bounds with Statistical Query and low-degree polynomial testing lower bounds, providing evidence that the sample-time-error tradeoffs achieved by our algorithms are qualitatively the best possible. Copyright © 2022, The Authors. All rights reserved.

关键词： consensus algorithm

Accelerating ERM for data-driven algorithm design using output-sensitive techniques

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Balcan, Maria-Florina Seiler, Christopher Sharma, Dravyansh Carnegie Mellon University United States

Data-driven algorithm design is a promising, learning-based approach for beyond worst-case analysis of algorithms with tunable parameters. An important open problem is the design of computationally efficient data-driven algorithms for combinatorial algorithm families with multiple parameters. As one fixes the problem instance and varies the parameters, the "dual" loss function typically has a piecewise-decomposable structure, i.e. is well-behaved except at certain sharp transition boundaries. Motivated by prior empirical work, we initiate the study of techniques to develop efficient ERM learning algorithms for data-driven algorithm design by enumerating the pieces of the sum dual loss functions for a collection of problem instances. The running time of our approach scales with the actual number of pieces that appear as opposed to worst case upper bounds on the number of pieces. Our approach involves two novel ingredients – an output-sensitive algorithm for enumerating polytopes induced by a set of hyperplanes using tools from computational geometry, and an execution graph which compactly represents all the states the algorithm could attain for all possible parameter values. We illustrate our techniques by giving algorithms for pricing problems, linkage-based clustering and dynamic-programming based sequence alignment. Copyright © 2022, The Authors. All rights reserved.

关键词： consensus algorithm

Decentralized Online Regularized Learning Over Random Time-Varying Graphs

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Zhang, Xiwei Li, Tao Fu, Xiaozheng School of Mathematical Sciences East China Normal University Shanghai200241 China

We study the decentralized online regularized linear regression algorithm over random time-varying graphs. At each time step, every node runs an online estimation algorithm consisting of an innovation term processing its own new measurement, a consensus term taking a weighted sum of estimations of its own and its neighbors with additive and multiplicative communication noises and a regularization term preventing over-fitting. It is not required that the regression matrices and graphs satisfy special statistical assumptions such as mutual independence, spatio-temporal independence or stationarity. We develop the nonnegative supermartingale inequality of the estimation error, and prove that the estimations of all nodes converge to the unknown true parameter vector almost surely if the algorithm gains, graphs and regression matrices jointly satisfy the sample path spatio-temporal persistence of excitation condition. Especially, this condition holds by choosing appropriate algorithm gains if the graphs are uniformly conditionally jointly connected and conditionally balanced, and the regression models of all nodes are uniformly conditionally spatio-temporally jointly observable, under which the algorithm converges in mean square and almost surely. In addition, we prove that the regret upper bound is O(T 1−τ lnT), where τ ∈ (0.5, 1) is a constant depending on the algorithm gains. © 2022, CC BY.

关键词： consensus algorithm

Public-Key Cryptography behind Blockchain Security

学校读者我要写书评

暂无评论

Public-Key Cryptography behind Blockchain Security

Networking, Information Systems and Security: Envisage Intelligent Systems in 5g//6G-based Interconnected Digital Worlds (NISS), International Conference on

作者： Mohamed Fartitchou Hanaa El Marraki Lamyae Lafkir Anissa Azzouz Khalid El Makkaoui Zakaria El Allali LaMAO laboratory MSC team FPD University Mohammed Premier Nador Morocco

Blockchain (BC) technology has infiltrated many areas of our lives, from digital currencies to healthcare and beyond, thanks to its security, decentralization, and transparency properties. BC is a decentralized ledger of assets and digitally signed transactions through a peer-to-peer (P2P) computer network. Indeed, signature and consensus algorithms are behind BC’s security and performance. This paper provides a study on BC-used asymmetric cryptography. We focus on RSA, DSA, and ECDSA digital signature algorithms. We implement the algorithms using python language on a Raspberry Pi. Simulation outcomes show that the ECDSA performs better than the other algorithms and is secure despite using small keys.

关键词： Online banking Computational modeling Medical services consensus algorithm Public key cryptography Blockchains Peer-to-peer computing

Implementation of Energy efficient Blockchain using RUST

学校读者我要写书评

暂无评论

Implementation of Energy efficient Blockchain using RUST

International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Amit Chaurasia Dhruv Rajesh Krishnamachari Department of Computer & Communication Engineering Manipal University Jaipur Jaipur Rajasthan India

ISBN: (纸本)9781665454025

Blockchain technology has become a popular technology since the introduction of WWW. With the introduction of Blockchain 2.0 and Blockchain 3.0 the limited applications of Blockchain increased exponentially. The efficient implementation of blockchain requires a lot of energy and computational resources, wherein the backend of the consensus algorithm implementation makes it power hungry. In this paper, we have introduced a new programming environment for the implementation of a consensus algorithm with requires less power and computational devices. The RUST-based implementation of the consensus algorithm makes it more power efficient

关键词： Computer languages consensus algorithm Programming Grid computing Energy efficiency Blockchains Computational efficiency

Distributed Energy Management for Port Microgrid with We-Energy

学校读者我要写书评

暂无评论

Distributed Energy Management for Port Microgrid with We-Ene...

Chinese Control and Decision Conference, CCDC

作者： Jianyuan Wang Fei Teng Tiankai Yang Qihe Shan Marine Electrical Engineering College Dalian Maritime University Dalian China Navigation College Dalian Maritime University Dalian China

ISBN: (纸本)9781665478977

In this paper, for the port microgrid with large-scale integration of renewable energy and clean energy, the energy management problem considering we-energies is investigated. Firstly, except the distributed generators and storage devices, we also regard green ships as we-energies, and obtain the cost function of various resources according to the port microgrid structure. Then considering the characteristic of two-way energy transmission between we-energies, the cost of the we-energies is analyzed and the energy management model of the whole port microgrid is established. Furthermore, a distributed energy management strategy is proposed to solve this problem. Finally, the feasibility of the proposed model and the effectiveness of the algorithm are verified by simulation.

关键词： Analytical models Renewable energy sources Costs Pollution Simulation Microgrids consensus algorithm