26 papers · ranked by Valyu relevance
Aryan Mokhtari, Hamed Hassani, Amin Karbasi
In this paper, we showcase the interplay between discrete and continuous optimization in networkstructured settings. We propose the first fully decentralized optimization method for a wide class of non-convex objective functions that possess a diminishing returns property. More specifically, given an arbitrary…
Yijie Zhou, Shi Pu
Decentralized optimization has emerged as a critical paradigm for distributed learning, enabling scalable training while preserving data privacy through peer-to-peer collaboration. However, existing methods often suffer from communication bottlenecks due to frequent synchronization between nodes. We present Overlapping…
Ziqin Chen, Zuang Wang, Yongqiang Wang
Decentralized optimization enables multiple devices to learn a global machine learning model while each individual device only has access to its local dataset. By avoiding the need for training data to leave individual users' devices, it enhances privacy and scalability compared to conventional centralized learning…
Bin Wang, Jun Fang, Huiping Duan, Hongbin Li
— We consider the problem of decentralized composite optimization over a symmetric connected graph, in which each node holds its own agent-specific private convex functions, and communications are only allowed between nodes with direct links. A variety of algorithms have been proposed to solve such a problem in an…
Yongqiang Wang, Tamer Başar
—By enabling multiple agents to cooperatively solve a global optimization problem in the absence of a central coordinator, decentralized stochastic optimization is gaining increasing attention in areas as diverse as machine learning, control, and sensor networks. Since the associated data usually contain sensitive…
Roula Nassif, Stefan Vlaski, Marco Carpentiero, Vincenzo Matta + 1 more
learning Authors: ['Roula Nassif' 'Stefan Vlaski' 'Marco Carpentiero' 'Vincenzo Matta' 'Ali H. Sayed'] Communication-constrained algorithms for decentralized learning and optimization rely on local updates coupled with the exchange of compressed signals. In this context, differential quantization is an effective…
Ming Xiao, Mikael Skoglund, H. Vincent Poor, Onur Günlü + 2 more
'Rafael F. Schaefer' 'Holger Boche'] This article aims to give a comprehensive and rigorous review of the principles and recent development of coding for large-scale distributed machine learning (DML). With increasing data volumes and the pervasive deployment of sensors and computing machines, machine learning has…
Qinghan Sun, Huan Ma, Tian Zhao, Yonglin Xin + 1 more
Distributed energy systems encompass a diverse range of generation and storage solutions on the user side, where decentralized management schemes to maximize the overall social welfare are preferred considering their dispersed ownership. However, either security or privacy problems occur in recently proposed schemes.…
Jing Ming, Eric Verner, Anand Sarwate, Ross Kelly + 7 more
'Torran Kahleck' 'Rogers Silva' 'Sandeep Panta' 'Jessica Turner' 'Sergey Plis' 'Vince Calhoun'] In the era of Big Data, sharing neuroimaging data across multiple sites has become increasingly important. However, researchers who want to engage in centralized, large-scale data sharing and analysis must often contend with…
Lie He, An Bian, Martin Jaggi
Decentralized machine learning is a promising emerging paradigm in view of global challenges of data ownership and privacy. We consider learning of linear classification and regression models, in the setting where the training data is decentralized over many user devices, and the learning algorithm must run ondevice…
Zhanhong Jiang, Aditya Balu, Chinmay Hegde, Soumik Sarkar
In distributed machine learning, where agents collaboratively learn from diverse private data sets, there is a fundamental tension between consensus and optimality. In this paper, we build on recent algorithmic progresses in distributed deep learning to explore various consensus-optimality trade-offs over a fixed…
Wei‐Neng Chen, Feng-Feng Wei, Tian-Fang Zhao, Kay Chen Tan + 1 more
'Jun Zhang'] Abstract—The rapid development of parallel and distributed computing paradigms has brought about great revolution in computing. Thanks to the intrinsic parallelism of evolutionary computation (EC), it is natural to implement EC on parallel and distributed computing systems. On the one hand, the computing…
Joshua Julian Damanik, Ming Chong Lim, Hyeon-Mun Jeong, Ho-Yeon Kim + 2 more
'Han-Lim Choi' 'Muhammad Aleem'] Multi-agent systems are promising for applications in various fields. However, they require optimization algorithms that can handle large number of agents and heterogeneously connected networks in clustered environments. Planning algorithms performed in the decentralized communication…
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…
Tingjun Lei, Pradeep Chintam, Chaomin Luo, Lantao Liu + 3 more
'David Cheneler' 'Stephen Monk'] In real-world applications, multiple robots need to be dynamically deployed to their appropriate locations as teams while the distance cost between robots and goals is minimized, which is known to be an NP-hard problem. In this paper, a new framework of team-based multi-robot task…
Sunitha Basodi, Rajikha Raja, Harshvardhan Gazula, Javier Tomas Romero + 4 more
Performing group analysis on magnetic resonance imaging (MRI) data with linear mixed-effects (LME) models is challenging due to its large dimensionality and inherent multi-level covariance structure. In addition, as large-scale collaborative projects become commonplace in neuroimaging, data must increasingly be stored…
Sunitha Basodi, Rajikha Raja, Bhaskar Ray, Harshvardhan Gazula + 3 more
Recent studies have demonstrated that neuroimaging data can be used to predict brain age, as it captures information about the neuroanatomical and functional changes the brain undergoes during development and the aging process. However, researchers often have limited access to neuroimaging data because of its…
Harshvardhan Gazula, Bharath Holla, Zuo Zhang, Jiayuan Xu + 4 more
In the recent past, there has been an upward trend in developing frameworks that enable neuroimaging researchers to address challenging questions by leveraging data across multiple sites all over the world. One such framework, Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation…
Noah Lewis, Harshvardhan Gazula, Sergey M. Plis, Vince D. Calhoun
In this age of big data, large data stores allow researchers to compose robust models that are accurate and informative. In many cases, the data are stored in separate locations requiring data transfer between local sites, which can cause various practical hurdles, such as privacy concerns or heavy network load. This…
Debbrata K. Saha, Rogers F. Silva, Bradley T. Baker, Rekha Saha + 1 more
The examination of multivariate brain morphometry patterns has gained attention in recent years, especially for their powerful exploratory capabilities in the study of differences between patients and controls. Among many existing methods and tools for analysis of brain anatomy based on structural magnetic resonance…
Authors not listed
With the ever-increasing demand for atomistic structures representative of real-life systems as well as the ad-vent of exascale computers, it has now become necessary and possible to use advanced global optimization (GO) techniques to intelligently sample the potential energy surface (PES). Given the previous studies…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Authors not listed
Solving optimization problems, especially for nonlinear and constrained systems, is a challenge. Decades of specialized algorithms have been developed for general and special cases of root finding, minimization (including constraints), for parameter estimation, and mapping connected spaces. These approaches typically…
AKHIL SHAJAN, Madushanka Manathunga, Andreas Goetz, Kenneth Merz
Based on a series of energy minimizations with starting structures obtained from the Baker test set of 30 organic molecules, a comparison is made between various open-source geometry optimization codes that are interfaced with the open-source QUantum Interaction Computational Kernel (QUICK) program for gradient and…
Akhil Shajan, Madushanka Manathunga, Andreas Goetz, Kenneth Merz
Based on a series of energy minimizations with starting structures obtained from the Baker test set of 30 organic molecules, a comparison is made between various open- source geometry optimization codes that are interfaced with the open-source QUantum Interaction Computational Kernel (QUICK) program for gradient and…