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A simple and communication-efficient Omega algorithm in the crash-recovery model

INFORMATION PROCESSING LETTERS 2010年第3期110卷 83-87页

作者： Martin, Cristian Larrea, Mikel Univ Basque Country San Sebastian 20018 Spain

This paper presents a new algorithm implementing the Omega failure detector in the crash-recovery model. Contrary to previously proposed algorithms, this algorithm does not rely on the use of stable storage and is communication-efficient, i.e., eventually only one process (the elected leader) keeps sending messages. The algorithm relies on a nondecreasing local clock associated with each process. Since stable storage is not used to keep the identity of the leader in order to read it upon recovery, unstable processes, i.e., those that crash and recover infinitely often, output a special perpendicular to value upon recovery, and then agree with correct processes on the leader after receiving a first message from it. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Distributed computing Fault tolerance Consensus Omega failure detector Leader election Crash-recovery communication-efficient algorithm

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SIGNGD with Error Feedback Meets Lazily Aggregated Technique:communication-efficient algorithms for Distributed Learning

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Tsinghua Science and Technology 2022年第1期27卷 174-185页

作者： Xiaoge Deng Tao Sun Feng Liu Dongsheng Li National Laboratory for Parallel and Distributed Processing(PDL) College of ComputerNational University of Defense TechnologyChangsha 410073China

The proliferation of massive datasets has led to significant interests in distributed algorithms for solving large-scale machine learning ***,the communication overhead is a major bottleneck that hampers the scalability of distributed machine learning *** this paper,we design two communication-efficient algorithms for distributed learning *** first one is named EF-SIGNGD,in which we use the 1-bit(sign-based) gradient quantization method to save the communication ***,the error feedback technique,i.e.,incorporating the error made by the compression operator into the next step,is employed for the convergence *** second algorithm is called LE-SIGNGD,in which we introduce a well-designed lazy gradient aggregation rule to EF-SIGNGD that can detect the gradients with small changes and reuse the outdated ***-SIGNGD saves communication costs both in transmitted bits and communication ***,we show that LE-SIGNGD is convergent under some mild *** effectiveness of the two proposed algorithms is demonstrated through experiments on both real and synthetic data.

关键词： distributed learning communication-efficient algorithm convergence analysis

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communication-efficient leader election in crash-recovery systems

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JOURNAL OF SYSTEMS AND SOFTWARE 2011年第12期84卷 2186-2195页

作者： Larrea, Mikel Martin, Cristian Soraluze, Iratxe Univ Basque Country UPV EHU San Sebastian 20018 Spain

This work addresses the leader election problem in partially synchronous distributed systems where processes can crash and recover. More precisely, it focuses on implementing the Omega failure detector class, which provides a leader election functionality, in the crash-recovery failure model. The concepts of communication efficiency and near-efficiency for an algorithm implementing Omega are defined. Depending on the use or not of stable storage, the property satisfied by unstable processes, i.e., those that crash and recover infinitely often, varies. Two algorithms implementing Omega are presented. In the first algorithm, which is communication-efficient and uses stable storage, eventually and permanently unstable processes agree on the leader with correct processes. In the second algorithm, which is near-communication-efficient and does not use stable storage, processes start their execution with no leader in order to avoid the disagreement among unstable processes, that will agree on the leader with correct processes after receiving a first message from the leader. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Fault-tolerant distributed computing Consensus Omega failure detector Leader election Crash-recovery communication-efficient algorithm

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communication-efficient Coordinated RSS-Based Distributed Passive Localization via Drone Cluster

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第1期71卷 1072-1076页

作者： Cheng, Xin Shi, Weiping Cai, Wenlong Zhu, Weiqiang Shen, Tong Shu, Feng Wang, Jiangzhou Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China China Aerosp Sci & Ind Corp Res Inst 8511 Nanjing 210007 Peoples R China Beijing Aerosp Automat Control Inst Natl Key Lab Sci & Technol Aerosp Intelligence Co Beijing 100854 Peoples R China Hainan Univ Sch Informat & Commun Engn Haikou 570228 Hainan Peoples R China Univ Kent Sch Engn & Digital Arts Canterbury CT2 7NT Kent England

Multi-UAV passive localization via received signal strength (RSS) is extremely important for wide applications such as rescue and battlefield combat. However, the energy consumption of UAVs is a key issue in this UAVs-enabled application. Usually, the communication overhead plays an important role in the energy consumption. To address this problem, we design two distributed methods for this multi-UAV system with considerable performance under low communication overhead. Firstly, a distributed majorize-minimization (DMM) method is proposed. To accelerate its convergence, a tight upper bound of the objective function from the primary one is derived. Furthermore, a distributed estimation scheme using the Fisher information matrix (DEF) is presented, only requiring one round of communication between edge UAVs and central UAV. Simulation results show that the proposed DMM outperforms the existing distributed iterative methods in terms of root of mean square error (RMSE) under low communication overhead. Moreover, the most communication-effective DEF with local search estimation performs much better than the proposed DMM in terms of RMSE, but has a higher computational complexity.

关键词： Location awareness Estimation Energy consumption Wireless sensor networks Trajectory Covariance matrices Probability density function Unmanned aerial vehicle (UAV) distributed localization communication-efficient algorithm majorize-minimization (MM) distributed estimation scheme

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communication-efficient String Sorting 34

Communication-Efficient String Sorting

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34th IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Bingmann, Timo Sanders, Peter Schimek, Matthias Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9781728168760

There has been surprisingly little work on algorithms for sorting strings on distributed-memory parallel machines. We develop efficient algorithms for this problem based on the multi-way merging principle. These algorithms inspect only characters that are needed to determine the sorting order. Moreover, communication volume is reduced by also communicating (roughly) only those characters and by communicating repetitions of the same prefixes only once. Experiments on up to 1280 cores reveal that these algorithm are often more than five times faster than previous algorithms.

关键词： distributed-memory algorithm string sorting communication-efficient algorithm

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Brief Announcement: Scalable Distributed String Sorting 24

Brief Announcement: Scalable Distributed String Sorting

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36th ACM Symposium on Parallelism in algorithms and Architectures (SPAA)

作者： Kurpicz, Florian Mehnert, Pascal Sanders, Peter Schimek, Matthias Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9798400704161

String sorting is an important part of tasks such as building index data structures. Unfortunately, current string sorting algorithms do not scale to massively parallel distributed-memory machines since they either have latency (at least) proportional to the number of processors.. or communicate the data a large number of times (at least logarithmic). We present practical and efficient algorithms for distributed-memory string sorting that scale to large p. Similar to state-of-the-art sorters for atomic objects, the algorithms have latency of about p(1/k) when allowing the data to be communicated k times. Experiments show good scaling behavior on a wide range of inputs on up to 49 152 cores. We achieve speedups of up to 5 over the current state-of-the-art distributed string sorting algorithms.

关键词： distributed-memory algorithm string sorting multi-level algorithm communication-efficient algorithm

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APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning 36

APPFL: Open-Source Software Framework for Privacy-Preserving...

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36th IEEE International Parallel and Distributed Processing Symposium (IEEE IPDPS)

作者： Ryu, Minseok Kim, Youngdae Kim, Kibaek Madduri, Ravi K. Argonne Natl Lab Math & Comp Sci Div Lemont IL 60439 USA Argonne Natl Lab Data Sci & Learning Div Lemont IL USA

ISBN: (纸本)9781665497473

Federated learning (FL) enables training models at different sites and updating the weights from the training instead of transferring data to a central location and training as in classical machine learning. The FL capability is especially important to domains such as biomedicine and smart grid, where data may not be shared freely or stored at a central location because of policy regulations. Thanks to the capability of learning from decentralized datasets, FL is now a rapidly growing research field, and numerous FL frameworks have been developed. In this work we introduce APPFL, the Argonne Privacy-Preserving Federated Learning framework. APPFL allows users to leverage implemented privacy-preserving algorithms, implement new algorithms, and simulate and deploy various FL algorithms with privacy-preserving techniques. The modular framework enables users to customize the components for algorithms, privacy, communication protocols, neural network models, and user data. We also present a new communication-efficient algorithm based on an inexact alternating direction method of multipliers. The algorithm requires significantly less communication between the server and the clients than does the current state of the art. We demonstrate the computational capabilities of APPFL, including differentially private FL on various test datasets and its scalability, by using multiple algorithms and datasets on different computing environments.

关键词： federated learning data privacy communication-efficient algorithm open-source software

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