Article
Computer Science, Theory & Methods
Enda Yu, Dezun Dong, Xiangke Liao
Summary: This paper proposes a standard for systematically classifying communication optimization algorithms in distributed deep learning systems based on mathematical modeling, which is a novel contribution in the field. The authors categorize existing works into four categories based on communication optimization strategies and discuss potential future challenges and research directions.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2023)
Article
Thermodynamics
Jun Yang, Fengyuan Sun, Haitao Wang
Summary: This paper proposes a distributed collaborative optimal dispatching strategy for the integrated energy system (IES) based on edge computing and consistency algorithm. It constructs a cloud-edge-device architecture and designs a distributed group consensus algorithm (DGCA) to solve the optimal dispatching problem of the IES, considering transmission loss.
Article
Computer Science, Artificial Intelligence
Huimin Guan, Yang Liu, Kit Ian Kou, Jinde Cao, Leszek Rutkowski
Summary: In this paper, a distributed optimization method is proposed to solve nonlinear equations with constraints. The multiple constrained nonlinear equations are transformed into an optimization problem and solved in a distributed manner. To deal with the nonconvexity issue, a multi-agent system based on an augmented Lagrangian function is introduced and proven to converge to a locally optimal solution. Moreover, a collaborative neurodynamic optimization method is adopted to obtain a globally optimal solution. The effectiveness of the proposed method is illustrated through three numerical examples.
Article
Computer Science, Artificial Intelligence
Bozhan Dang, Yingxu Wang, Jin Zhou, Rongrong Wang, Long Chen, C. L. Philip Chen, Tong Zhang, Shiyuan Han, Lin Wang, Yuehui Chen
Summary: In this article, a novel series of transfer collaborative fuzzy clustering algorithms are proposed to address the privacy, security, and network transmission technology limitations of traditional clustering methods in distributed peer-to-peer networks. The algorithms enhance information collaboration using transfer learning among neighbor nodes, accelerate the convergence of fuzzy clustering, and ensure stable clustering accuracy through adjustable learning rates. Two extended versions are also presented for clustering high dimensional sparse data and extracting important subspace features.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Giovanni Acampora, Ferdinando Di Martino, Alfredo Massa, Roberto Schiattarella, Autilia Vitiello
Summary: This paper introduces the concept of Distributed Noisy-Intermediate Scale Quantum (D-NISQ) as a reference computational model to design innovative frameworks for quantum devices to interact and solve complex problems collaboratively. Through two case studies, a multi-threaded implementation of the D-NISQ model demonstrates greater reliability in solving problems through quantum computation.
INFORMATION FUSION
(2023)
Article
Engineering, Multidisciplinary
Shijing Li, Tian Lan, Bharath Balasubramanian, Hee Won Lee, Moo-Ryong Ra, Rajesh Krishna Panta
Summary: Edge computing has experienced explosive growth and is a new paradigm. Data deduplication at the network edge allows for utilizing geographic and temporal correlations in data to save space and bandwidth. By leveraging collaborative distributed computing power, edge nodes can effectively remove duplicated data.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2022)
Article
Engineering, Electrical & Electronic
Hao Feng, Songtao Guo, Li Yang, Yuanyuan Yang
Summary: This paper investigates how to maximize storage utilization while reducing service latency and energy consumption in edge networks, proposing a two-tier MEC system that optimizes data caching and computing offloading policies to minimize network costs at the user equipment side.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Robotics
Yulun Tian, Kasra Khosoussi, Jonathan P. How
Summary: The paper introduces resource-aware algorithms for distributed inter-robot loop-closure detection, addressing resource challenges faced by small-size and low-cost robots in loop-closure detection.
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Yingxu Wang, Shiyuan Han, Jin Zhou, Long Chen, C. L. Philip Chen, Tong Zhang, Zhulin Liu, Lin Wang, Yuehui Chen
Summary: This article proposes a series of random feature-based collaborative kernel clustering algorithms to address the issues of high computational complexity and unknown representation in distributed P2P networks. The algorithms map data into a low-dimensional random feature space, and each node independently searches clusters using local data and collaborative knowledge. Feature weights are assigned to optimize cluster identification, and multiple kernels are combined for low-dimensional approximation. Experimental results show that the proposed methods achieve similar or better results than traditional kernel clustering methods, with lower temporal complexity.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Information Systems
Lei Dai, Liming Zhang, Zehua Chen, Weiping Ding
Summary: This paper proposes a novel deterministic multi-EAs method for multimodal optimization problems. By introducing the principle of global optimization and establishing a survival-of-the-fittest strategy, it achieves effective solutions to multimodal optimization problems without the need for random parameters.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Jordan J. Romvary, Giulio Ferro, Rabab Haider, Anuradha M. Annaswamy
Summary: This article introduces a unified framework for distributed convex optimization using a algorithm called proximal atomic coordination (PAC). The convergence of PAC is proven in both objective values and distance to feasibility. Various decomposition strategies and coordination graphs are explored in relation to the convergence rate of PAC. Additionally, the algorithmic complexity of PAC is compared with another popular distributed algorithm. The advantages of PAC are enumerated, including its relevance to privacy. The theoretical results are validated using a power distribution grid model in the context of the optimal power flow problem.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Automation & Control Systems
Guido Carnevale, Francesco Farina, Ivano Notarnicola, Giuseppe Notarstefano
Summary: This article presents a network of computing agents that aims to solve an online optimization problem in a distributed manner, without the need for a central coordinator. The proposed GTAdam algorithm combines a gradient tracking mechanism with first- and second-order momentum estimates of the gradient. It is analyzed in the online setting for strongly convex cost functions with Lipschitz continuous gradients and is found to outperform state-of-the-art distributed optimization methods in numerical experiments.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2023)
Article
Computer Science, Information Systems
Hangyu Zhang, Rongke Liu, Aryan Kaushik, Xiangqiang Gao
Summary: This article proposes a three-tier edge computing architecture consisting of terminal-satellite-cloud, where tasks can be processed at three planes and intersatellites can cooperate for load balancing. The objective problem of minimizing system energy consumption under delay and resource constraints is formulated, and offloading decision, communication, and computing resource allocation variables are jointly optimized. An intelligent computation offloading scheme based on deep deterministic policy gradient (DDPG) algorithm is proposed to adapt to the dynamic environment and enable simultaneous decisions on offloading locations and resource allocation under multitask concurrency. Simulation results demonstrate the effectiveness of the proposed scheme in reducing total energy consumption and outperforming benchmark algorithms.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Rongping Lin, Tianze Xie, Shan Luo, Xiaoning Zhang, Yong Xiao, Bill Moran, Moshe Zukerman
Summary: This study focuses on the computation offloading problem in collaborative edge computing networks and proposes a collaborative load shedding approach to optimize computation offloading and resource allocation, achieving more efficient computing services. Theoretical analysis and numerical results demonstrate that the distributed algorithm can achieve guaranteed long-term performance and improve the performance of computation offloading.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Engineering, Multidisciplinary
Wali Khan Mashwani, Habib Shah, Manjit Kaur, Maharani Abu Bakar, Miftahuddin Miftahuddin
Summary: Evolutionary computing, including many algorithms based on Darwinian principles, has been a focus of soft computing in the past two decades. Teaching Learning based Optimization (TLBO), a recently developed algorithm, employs a group of learners for global optimization search. The framework of TLBO consists of two phases: Teacher Phase and Learner Phase, focusing on learning from teachers and interaction among learners respectively.
ALEXANDRIA ENGINEERING JOURNAL
(2021)
Article
Automation & Control Systems
Dusan Jakovetic, Jose M. F. Moura, Joao Xavier
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2015)
Article
Engineering, Electrical & Electronic
Dragana Bajovic, Jose M. F. Moura, Joao Xavier, Bruno Sinopoli
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2016)
Article
Mathematics, Applied
Marko Stosic, Joao Xavier, Marija Dodig
LINEAR ALGEBRA AND ITS APPLICATIONS
(2016)
Article
Engineering, Electrical & Electronic
Pedro Guerreiro, Joao Xavier
IEEE SIGNAL PROCESSING LETTERS
(2017)
Article
Automation & Control Systems
Brian Swenson, Soummya Kar, Joao Xavier
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2017)
Article
Automation & Control Systems
Brian Swenson, Soummya Kar, Joao Xavier, David S. Leslie
SIAM JOURNAL ON CONTROL AND OPTIMIZATION
(2017)
Article
Engineering, Electrical & Electronic
Antonio Simoes, Joao Xavier
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2019)
Article
Engineering, Electrical & Electronic
Joao Domingos, Claudia Soares, Joao Xavier
Summary: This article presents a localization method based on bounded noise and unknown distributions, creating a tight superset using convex relaxations and linear fractional representations. The superset provides accurate target location in high-risk real-world scenarios.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2022)
Article
Engineering, Electrical & Electronic
Dusan Jakovetic, Dragana Bajovic, Joao Xavier, Jose M. F. Moura
PROCEEDINGS OF THE IEEE
(2020)
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Engineering, Electrical & Electronic
Lucas Balthazar, Joao Xavier, Bruno Sinopoli
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2020)
Proceedings Paper
Automation & Control Systems
Brian Swenson, Soummya Kar, Joao Xavier
2015 54TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC)
(2015)
Proceedings Paper
Computer Science, Theory & Methods
Sabina Zejnilovic, Joao Xavier, Joao Gomes, Bruno Sinopoli
2015 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY (ISIT)
(2015)
Proceedings Paper
Engineering, Electrical & Electronic
Joao S. Lemos, Francisco Rosario, Francisco A. Monteiro, Joao Xavier, Antonio Rodrigues
2015 IEEE 16TH INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (SPAWC)
(2015)
Proceedings Paper
Engineering, Electrical & Electronic
Brian Swenson, Soummya Kar, Joao Xavier
2015 23RD EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO)
(2015)