16 papers · ranked by Valyu relevance
Artin Spiridonoff, Alex Olshevsky, Ioannis Ch. Paschalidis
We consider the standard model of distributed optimization of a sum of functions $F(z)=\sum_{i=1}^{n}f_{i}(z)$, where node i in a network holds the function fi(z). We allow for a harsh network model characterized by asynchronous updates, message delays, unpredictable message losses, and directed communication among…
Feilong Guo, Xinrui Chen, Mengyao Yue, Haijun Jiang + 2 more
'António Lopes'] This study aims to unravel the resource allocation problem (RAP) by using a consensus-based distributed optimization algorithm under dynamic event-triggered (DET) strategies. Firstly, based on the multi-agent consensus approach, a novel one-to-all DET strategy is presented to solve the RAP. Secondly…
Maximilian Alber, Julian Zimmert, Urun Dogan, Marius Kloft + 1 more
Training of one-vs.-rest SVMs can be parallelized over the number of classes in a straight forward way. Given enough computational resources, one-vs.-rest SVMs can thus be trained on data involving a large number of classes. The same cannot be stated, however, for the so-called all-in-one SVMs, which require solving a…
Liping Feng, Liang Ran, Guoyang Meng, Jialong Tang + 3 more
'Huaqing Li' 'Shu-Chuan Chu'] In this paper, we focus on the nonsmooth composite optimization problems over networks, which consist of a smooth term and a nonsmooth term. Both equality constraints and box constraints for the decision variables are also considered. Based on the multi-agent networks, the objective…
Duan Peibo, Zhang Changsheng, Zhang Bin
This paper presents a new distributed constraint optimization algorithm called LSPA, which can be used to solve large scale distributed constraint optimization problem (DCOP). Different from the access of local information in the existing algorithms, a new criterion called local stability is defined and used to…
Shuo Wang, Yongcai Wang, Deying Li, Qianchuan Zhao + 1 more
For a network of robots working in a specific environment, relative localization among robots is the basis for accomplishing various upper-level tasks. To avoid the latency and fragility of long-range or multi-hop communication, distributed relative localization algorithms, in which robots take local measurements and…
Dinesh Krishnamoorthy
This paper proposes a general-purpose multi-agent Bayesian optimization (MABO) where agents are connected via shared variables or constraints, and each agent’s local cost is unknown. The proposed approach is general-purpose in the sense that it can be used with a broad class of decomposition methods, whereby we augment…
Meifeng Shi, Feipeng Liang, Yuan Chen, Ying He + 1 more
As an important incomplete algorithm for solving Distributed Constraint Optimization Problems (DCOPs), local search algorithms exhibit the advantages of flexibility, high efficiency and high fault tolerance. However, the significant historical values of agents that affect the local cost and global cost are never taken…
Shiwa Chen, Jianyun Zhang, Yunxiang Mao, Chengcheng Xu + 1 more
The accuracy of cooperative localization can be severely degraded in non-line-of-sight (NLOS) environments. Although most existing approaches modify models to alleviate NLOS impact, computational speed does not satisfy practical applications. In this paper, we propose a distributed cooperative localization method for…
Yuan Wu, Xiangxu Chen, Jiajun Shi, Kejie Ni + 3 more
'Liang Huang' 'Kuan Zhang'] Blockchain has emerged as a decentralized and trustable ledger for recording and storing digital transactions. The mining process of Blockchain, however, incurs a heavy computational workload for miners to solve the proof-of-work puzzle (i.e., a series of the hashing computation), which is…
Matheus Sant’Ana Lima, Seyedali Mirjalili
Distributed Systems architectures are becoming the standard computational model for processing and transportation of information, especially for Cloud Computing environments. The increase in demand for application processing and data management from enterprise and end-user workloads continues to move from a single-node…
Haijie Pan, Lirong Zheng, Sylvain Girard
Machine learning models often converge slowly and are unstable due to the significant variance of random data when using a sample estimate gradient in SGD. To increase the speed of convergence and improve stability, a distributed SGD algorithm based on variance reduction, named DisSAGD, is proposed in this study.…
Emre Ozfatura, Sennur Ulukus, Deniz Gündüz
When gradient descent (GD) is scaled to many parallel workers for large-scale machine learning applications, its per-iteration computation time is limited by straggling workers. Straggling workers can be tolerated by assigning redundant computations and/or coding across data and computations, but in most existing…
Huiguo Gao, Mengyuan Lee, Guanding Yu, Zhaolin Zhou + 1 more
As an emerging paradigm considering data privacy and transmission efficiency, decentralized learning aims to acquire a global model using the training data distributed over many user devices. It is a challenging problem since link loss, partial device participation, and non-independent and identically distributed…
António Luís Lopes, Luís Miguel Botelho, Olaf Sporns
In this paper, we describe a distributed coordination system that allows agents to seamlessly cooperate in problem solving by partially contributing to a problem solution and delegating the subproblems for which they do not have the required skills or knowledge to appropriate agents. The coordination mechanism relies…
Ivan Rodriguez-Conde, Celso Campos, Florentino Fdez-Riverola, Antonio Fernández-Caballero + 1 more
'Antonio Fernández-Caballero' 'Juan M. Corchado'] Motivated by the pervasiveness of artificial intelligence (AI) and the Internet of Things (IoT) in the current “smart everything” scenario, this article provides a comprehensive overview of the most recent research at the intersection of both domains, focusing on the…