Search Results - "Communication-efficient algorithm"

A Communication-Efficient Algorithm for Federated Multilevel Stochastic Compositional Optimization by Yang, Shuoguang, Li, Fengpei

“… Yet, it remains unclear whether communication-efficient algorithms exist for MSCO in distributed settings…”
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M22: A Communication-Efficient Algorithm for Federated Learning Inspired by Rate-Distortion by Liu, Yangyi, Rini, Stefano, Salehkalaibar, Sadaf, Chen, Jun

“…In federated learning (FL), the communication constraint between the remote clients and the Parameter Server (PS) is a crucial bottleneck. For this reason,…”
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Memory and communication efficient algorithm for decentralized counting of nodes in networks by Saha, Arindam, Marshall, James A. R., Reina, Andreagiovanni

“…Node counting on a graph is subject to some fundamental theoretical limitations, yet a solution to such problems is necessary in many applications of graph…”
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Fast and Communication-Efficient Algorithm for Distributed Support Vector Machine Training by Dass, Jyotikrishna, Sarin, Vivek, Mahapatra, Rabi N.

“…Support Vector Machines (SVM) are widely used as supervised learning models to solve the classification problem in machine learning. Training SVMs for large…”
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Decentralized and Collaborative Subspace Pursuit: A Communication-Efficient Algorithm for Joint Sparsity Pattern Recovery With Sensor Networks by Gang Li, Wimalajeewa, Thakshila, Varshney, Pramod K.

“…In this paper, we consider the problem of joint sparsity pattern recovery in a distributed sensor network. The sparse multiple measurement vector signals…”
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Communication-Efficient Coordinated RSS-Based Distributed Passive Localization via Drone Cluster by Cheng, Xin, Shi, Weiping, Cai, Wenlong, Zhu, Weiqiang, Shen, Tong, Shu, Feng, Wang, Jiangzhou

“…Multi-UAV passive localization via received signal strength (RSS) is extremely important for wide applications such as rescue and battlefield combat. However,…”
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GR-ADMM: A Communication Efficient Algorithm Based on ADMM by Huang, Xin, Wang, Guozheng, Lei, Yongmei

“…In recent work, the decentralized algorithm has received more attention. In the centralized network, the worker nodes need to communicate with the central…”
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Communication-Efficient Decentralized Task Allocation for Large-Scale Multi-Agent Systems by Wang, Shengli, Li, Simin, Huangfu, Yafan, Qiu, Yongtao, Liu, Youjiang

“…This letter presents a novel method to solve the decentralized task allocation problem for large-scale multi-agent systems (MASs), with an emphasis on…”
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A Communication-Efficient Algorithm for Exponentially Fast Non-Bayesian Learning in Networks by Mitra, Aritra, Richards, John A., Sundaram, Shreyas

“…We introduce a simple time-triggered protocol to achieve communication-efficient non-Bayesian learning over a network. Specifically, we consider a scenario…”
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A Proximal Gradient Method With Probabilistic Multi-Gossip Communications for Decentralized Composite Optimization by Guo, Luyao, Wang, Luqing, Shi, Xinli, Cao, Jinde

“…Decentralized optimization methods with local updates have recently gained attention for their provable ability to communication acceleration. In these…”
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Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis by Crawshaw, Michael, Liu, Mingrui

“…In federated learning, it is common to assume that clients are always available to participate in training, which may not be feasible with user devices in…”
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A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning by Sun, Zhenyu, Wei, Ermin

“…In this paper, we study a large-scale multi-agent minimax optimization problem, which models many interesting applications in statistical learning and game…”
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GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data by Zhang, Xinwei, Hong, Mingyi, Chen, Jie

“…Vertical federated learning (VFL) is a distributed learning paradigm, where computing clients collectively train a model based on the partial features of the…”
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M22: A Communication-Efficient Algorithm for Federated Learning Inspired by Rate-Distortion by Liu, Yangyi, Rini, Stefano, Salehkalaibar, Sadaf, Chen, Jun

“…In federated learning (FL), the communication constraint between the remote learners and the Parameter Server (PS) is a crucial bottleneck. For this reason,…”
Get full text

A memory and communication efficient algorithm for decentralized counting of nodes in networks by Saha, Arindam, Marshall, James A R, Andreagiovanni Reina

“…Node counting on a graph is subject to some fundamental theoretical limitations, yet a solution to such problems is necessary in many applications of graph…”
Get full text

D-FW: Communication efficient distributed algorithms for high-dimensional sparse optimization by Lafond, Jean, Hoi-To Wai, Moulines, Eric

“… be sparse during the intermediate steps of optimization. The novelty of this work is to develop communication efficient algorithms using the stochastic Frank-Wolfe (sFW…”
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APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning by Ryu, Minseok, Kim, Youngdae, Kim, Kibaek, Madduri, Ravi K.

“…Federated learning (FL) enables training models at different sites and updating the weights from the training instead of transferring data to a central…”
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A Communication-Efficient Algorithm for Exponentially Fast Non-Bayesian Learning in Networks by Mitra, Aritra, Richards, John A, Sundaram, Shreyas

“…We introduce a simple time-triggered protocol to achieve communication-efficient non-Bayesian learning over a network. Specifically, we consider a scenario…”
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Communication-efficient leader election in crash–recovery systems by Larrea, Mikel, Martín, Cristian, Soraluze, Iratxe

“…• We implement Omega in crash–recovery partially synchronous distributed systems. • A first algorithm uses stable storage and is communication-efficient. • A…”
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Decentralized and Collaborative Subspace Pursuit: A Communication-Efficient Algorithm for Joint Sparsity Pattern Recovery with Sensor Networks by Li, Gang, Wimalajeewa, Thakshila, Varshney, Pramod K

“…In this paper, we consider the problem of joint sparsity pattern recovery in a distributed sensor network. The sparse multiple measurement vector signals…”
Get full text