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Keyword

communication efficiency

Description: Communication efficiency is the effectiveness with which information is transmitted, particularly in distributed computing and spoken communication.
OpenAlex ID: keywords/communication-efficiency
Communication-Efficient Learning of Deep Networks from Decentralized Data
2016 · McMahan, H. Brendan, Moore, Eider, et al. · arXiv (Cornell University) · 14,215
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Practical Secure Aggregation for Privacy-Preserving Machine Learning
2017 · Keith Bonawitz, Vladimir Ivanov, et al. · ACM SIGSAC Conference on Computer and Communications Security (CCS) · 3,925
Federated Learning: Strategies for Improving Communication Efficiency
2016 · Konečný, Jakub, McMahan, H. Brendan, et al. · arXiv (Cornell University) · 3,658
Long-distance quantum communication with atomic ensembles and linear optics
2001 · L.-M. Duan, M. D. Lukin, et al. · Nature · 3,610
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SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
2019 · Karimireddy, Sai Praneeth, Kale, Satyen, et al. · arXiv (Cornell University) · 1,732
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Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data
2019 · Felix Sattler, Simon Wiedemann, et al. · IEEE Transactions on Neural Networks and Learning Systems · 1,718
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On the Convergence of FedAvg on Non-IID Data
2019 · Li, Xiang, Huang, Kaixuan, et al. · arXiv (Cornell University) · 1,575
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Federated Optimization: Distributed Machine Learning for On-Device Intelligence
2016 · Konečný, Jakub, McMahan, H. Brendan, et al. · arXiv (Cornell University) · 1,563
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Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
2018 · Yin, Dong, Chen, Yudong, et al. · arXiv (Cornell University) · 1,130
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Optimizing Federated Learning on Non-IID Data with Reinforcement Learning
2020 · Hao Wang, Zakhary Kaplan, et al. · 1,052
Gaining from vertical partnerships: knowledge transfer, relationship duration, and supplier performance improvement in the U.S. and Japanese automotive industries
2002 · Masaaki Kotabe, Xavier Martin, et al. · Strategic Management Journal · 944
Federated Learning of Deep Networks using Model Averaging
2016 · H. Brendan McMahan, Eider Moore, et al. · arXiv (Cornell University) · 925
Efficient Multiparty Protocols Using Circuit Randomization
2007 · Donald Beaver · Springer eBooks · 896
Adaptive Federated Optimization
2020 · Reddi, Sashank, Charles, Zachary, et al. · arXiv (Cornell University) · 840
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PipeDream
2019 · Deepak Narayanan, Aaron Harlap, et al. · 820
Federated learning with hierarchical clustering of local updates to improve training on non-IID data
2020 · Christopher Briggs, Zhong Fan, et al. · 745
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SplitFed: When Federated Learning Meets Split Learning
2022 · Chandra Thapa, Pathum Chamikara Mahawaga Arachchige, et al. · Proceedings of the AAAI Conference on Artificial Intelligence · 740
Federated Learning with Matched Averaging
2020 · Wang, Hongyi, Yurochkin, Mikhail, et al. · arXiv (Cornell University) · 725
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Ensemble Distillation for Robust Model Fusion in Federated Learning
2020 · Lin, Tao, Kong, Lingjing, et al. · arXiv (Cornell University) · 711
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Word lengths are optimized for efficient communication
2011 · Steven T. Piantadosi, Harry Tily, et al. · Proceedings of the National Academy of Sciences · 695
Local SGD Converges Fast and Communicates Little
2018 · Stich, Sebastian U. · arXiv (Cornell University) · 680
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Privacy Preserving Data Mining
2002 · Lindell, Pinkas · Journal of Cryptology · 668
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Communication-efficient federated learning via knowledge distillation
2022 · Chuhan Wu, Fangzhao Wu, et al. · Nature Communications · 623
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Deep Anomaly Detection for Time-Series Data in Industrial IoT: A Communication-Efficient On-Device Federated Learning Approach
2020 · Yi Liu, Sahil Garg, et al. · IEEE Internet of Things Journal · 592
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Model Pruning Enables Efficient Federated Learning on Edge Devices
2022 · Yuang Jiang, Shiqiang Wang, et al. · IEEE Transactions on Neural Networks and Learning Systems · 591
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Works count: 12,280
Citation count: 175,200
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