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A Survey on Fault-tolerance in Distributed Optimization and Machine Learning [article]

Shuo Liu
2021 arXiv   pre-print
With the rapid expansion of the scale of distributed systems, resilient distributed algorithms for optimization are needed, in order to mitigate system failures, communication issues, or even malicious  ...  The robustness of distributed optimization is an emerging field of study, motivated by various applications of distributed optimization including distributed machine learning, distributed sensing, and  ...  Gupta and Vaidya [45, 46] studied a different type of redundancy in cost functions named 2f -redundancy, defined as follows: Definition 1 (2f -redundancy) The cost functions of a set of non-faulty agents  ... 
arXiv:2106.08545v2 fatcat:g6fys4icrbbr5k3bd3ycylaptu

Artificial Intelligence Assisted Power Grid Hardening in Response to Extreme Weather Events [article]

Rozhin Eskandarpour, Amin Khodaei, A. Paaso, N. M. Abdullah
2018 arXiv   pre-print
In contrast to existing literature in hardening and resilience enhancement, this paper co-optimizes grid economic and resilience objectives by considering the intricate dependencies of the two.  ...  Then, these predictions are fed into a hardening model, which determines strategic locations for placement of distributed generation (DG) units.  ...  Machine learning approaches are utilized in a considerable number of research efforts in the power and energy sector, such as security assessment [6] , load forecasting [7] , distribution fault detection  ... 
arXiv:1810.02866v1 fatcat:b52x3bzgc5atzbk5a2od262m2y

Approximate Byzantine Fault-Tolerance in Distributed Optimization [article]

Shuo Liu, Nirupam Gupta, Nitin H. Vaidya
2021 arXiv   pre-print
We obtain necessary and sufficient conditions for achieving (f,ϵ)-resilience characterizing the correlation between relaxation in redundancy and approximation in resilience.  ...  In case when the agents' cost functions are differentiable, we obtain conditions for (f,ϵ)-resilience of the distributed gradient-descent method when equipped with robust gradient aggregation.  ...  The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory  ... 
arXiv:2101.09337v4 fatcat:jlhclmf2ljhzvlaf6almnqdyri

Resilience in Collaborative Optimization: Redundant and Independent Cost Functions [article]

Nirupam Gupta, Nitin H. Vaidya
2020 arXiv   pre-print
We present an algorithm that attains weak resilience when the faulty agents are in the minority and the cost functions are non-negative.  ...  In this report, we show that this goal can be achieved if and only if the cost functions of the non-faulty agents have a minimal redundancy property.  ...  Acknowledgements Research reported in this paper was sponsored in part by the Army Research Laboratory under Cooperative Agreement W911NF-17-2-0196, and by National Science Foundation award 1842198.  ... 
arXiv:2003.09675v2 fatcat:fobpzqkt6zf2rl6bhdhbuf5isu

Reliable Distributed Clustering with Redundant Data Assignment [article]

Venkata Gandikota, Arya Mazumdar, Ankit Singh Rawat
2020 arXiv   pre-print
The assignment scheme leads to distributed algorithms with good approximation guarantees for a variety of clustering and dimensionality reduction problems.  ...  In this paper, we present distributed generalized clustering algorithms that can handle large scale data across multiple machines in spite of straggling or unreliable machines.  ...  Second, and more importantly, the utilization of the redundant data assignment in this work (cf.  ... 
arXiv:2002.08892v1 fatcat:lai7glctnvh63nvjodssn3b4vm

Resilience Thinking as an Interdisciplinary Guiding Principle for Energy System Transitions

Frauke Wiese
2016 Resources  
The seven principles of resilience thinking (maintain redundancy and diversity, manage connectivity, manage slow variables and feedback, foster complex adaptive systems thinking, encourage learning, broaden  ...  Concepts focusing on process and building capacity can be found in resilience theory [16] . This concept has experienced a wide distribution of applications across disciplines.  ...  The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  ... 
doi:10.3390/resources5040030 fatcat:ulqs4raywze5lldi7dhe6xwtha

Byzantine Fault Tolerance in Distributed Machine Learning : a Survey [article]

Djamila Bouhata, Hamouma Moumen
2022 arXiv   pre-print
Byzantine Fault Tolerance (BFT) is among the most challenging problems in Distributed Machine Learning (DML).  ...  In this paper, we present a survey of recent works surrounding BFT in DML. Mainly in first-order optimization methods, especially Stochastic Gradient Descent (SGD).  ...  It calculates the gradient of the cost function based on the complete training set for every update. Therefore, GD can be very slow and intractable in practice.  ... 
arXiv:2205.02572v1 fatcat:h2hkcgz3w5cvrnro6whl2rpvby

Managing Capabilities for Supply Chain Resilience Through it Integration

Valentas Gružauskas, Mantas Vilkas
2017 Economics and Business  
Collaboration, flexibility, redundancy and integration are the most influential capabilities to supply chain resilience.  ...  These trends changed the distribution process: delivery distances are decreasing, the product variety is increasing, and more products are being sold in smaller quantities.  ...  This research provides evidence that the collaboration, redundancy and flexibility capabilities must be addressed in a more specific way in order to better utilize them for supply chain resilience.  ... 
doi:10.1515/eb-2017-0016 fatcat:adtanzhk5vgvdmpg4nowq77654

Guest Editorial: Special Issue on Communications and Data Analytics in the Smart Grid

Ying-Jun Angela Zhang, Hans-Peter Schwefel, Hamed Mohsenian-Rad, Christian Wietfeld, Chen Chen, Hamid Gharavi
2020 IEEE Journal on Selected Areas in Communications  
quality, and variety of data that utilities and grid operators are collecting on supply, transmission, distribution, and demand.  ...  Big data analytics in Smart Grids transform the large volume of data into meaningful inputs that help to predict and understand end-customer behavior, improve network resilience and faults, enhance security  ...  finding optimal trade-offs between cost and resilience.  ... 
pmid:33029039 pmcid:PMC7537467 fatcat:vgblruonejb65ewidldzjracnq

Guest Editorial Special Issue on Communications and Data Analytics in Smart Grid

Ying-Jun Angela Zhang, Hans-Peter Schwefel, Hamed Mohsenian-Rad, Christian Wietfeld, Chen Chen, Hamid Gharavi
2020 IEEE Journal on Selected Areas in Communications  
finding optimal trade-offs between cost and resilience.  ...  Algorithms are developed both for the case where the WAMS system is under design, and for the case where it already exists and needs to be equipped with some form of redundancy to enhance resilience of  ... 
doi:10.1109/jsac.2019.2952769 fatcat:hjsyk4ttyzf5dfyaoakuvgrbbu

A Survey on Resiliency Techniques in Cloud Computing Infrastructures and Applications

Carlos Colman-Meixner, Chris Develder, Massimo Tornatore, Biswanath Mukherjee
2016 IEEE Communications Surveys and Tutorials  
The third part focuses on resilience in application design and development.  ...  The second major part of the paper introduces and categorizes a large number of techniques for cloud computing infrastructure resiliency.  ...  .c) Passive redundancy scheduling for resiliency: consists in the utilization of passive replications of traffic or jobs on servers and VMs to provide resiliency.  ... 
doi:10.1109/comst.2016.2531104 fatcat:vzvkai7nkrbbda63fesn7zw4di

Power System Resilience: Current Practices, Challenges, and Future Directions

Narayan Bhusal, Michael Abdelmalak, MD Kamruzzaman, Mohammed Benidris
2020 IEEE Access  
The first step toward this goal is to develop resilience metrics and evaluation methods to compare planning and operation alternatives and to provide techno-economic justifications for resilience enhancement  ...  ., substations, transmission lines, and power plants) destructions. This calls for developing control and operation methods and planning strategies to improve grid resilience against such events.  ...  operating and load shedding penalty costs in emergency mode [76] ; (vii) network upgrade cost with limit on load shedding [96] ; and (viii) simulation-based optimization function for both PV and battery  ... 
doi:10.1109/access.2020.2968586 fatcat:ym3opz6ijbaujhyrhzpiqk5kxa

Discriminatively Fortified Computing with Reconfigurable Digital Fabric

Mingjie Lin, Yu Bai, John Wawrzynek
2011 2011 IEEE 13th International Symposium on High-Assurance Systems Engineering  
error resilience exists inherently in many practical algorithms, such as signal processing, visual perception, and artificial learning.  ...  heuristic methodology to discriminatively allocate hardware redundancy among a target design's key components in order to maximize its overall error resilience, 3) an academic prototype of DFC computing  ...  Solving Allocation Problem In this section we determine the optimal allocation for a generic hardware design subject to a set of hardware redundancy with various costs, i.e, solving the optimization problem  ... 
doi:10.1109/hase.2011.49 dblp:conf/hase/LinBW11 fatcat:gflhnleahbd7ngocfujfbhbmeu

Meta-Analysis of the Strategies for Self-healing and Resilience in Power Systems

Ekundayo Shittu, Abhijai Tibrewala, Swetha Kalla, Xiaonan Wang
2021 Advances in Applied Energy  
While there is significant coverage and convergence of research on algorithms for solving the multi-objective problem in optimization routines, there are still uncharted territories on how to incorporate  ...  identify gaps in the literature on integration • Meta-heuristic shows vacuum includes limited degradation and self-restoration analysis • Models for a smarter, cleaner and more resilient systems are required  ...  reducing system redundancies and consequently keeping the cost constraints in check.  ... 
doi:10.1016/j.adapen.2021.100036 fatcat:hvgmkb7cqrhmlgg6ilzzw7gvey

POSE.R: Prediction-based Opportunistic Sensing for Resilient and Efficient Sensor Networks [article]

James Z. Hare, Junnan Song, Shalabh Gupta, Thomas A. Wettergren
2020 arXiv   pre-print
The paper presents a distributed algorithm, called Prediction-based Opportunistic Sensing for Resilient and Efficient Sensor Networks (POSE.R), where the sensor nodes utilize predictions of the targets  ...  If the target is traveling through a high node density area, then an optimal sensor selection approach is employed that maximizes a joint cost function of remaining energy and geometric diversity around  ...  The goal of the Maxlogit learning algorithm is to identify the Nash equilibrium of the potential function. Therefore, s Lead utilizes the utility function of Eq.  ... 
arXiv:1910.10795v2 fatcat:s7idkknumjd3dn6rixlbbtkd6q
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