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Hierarchical SGD (H-SGD) has emerged as a new distributed SGD algorithm for multi-level communication networks.
A distributed hierarchical SGD algorithm with sparse global reduction
Zhou, F.; and Cong, G. 2019 · 1903
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Federated learning: Challenges, methods, and future directions
Li, T.; Sahu, A. K.; Talwalkar, A.; and Smith, V. 2019 · 1908
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Adaptive Federated Optimization
Reddi, S.; Charles, Z.; Zaheer, M.; Garrett, Z.; Rush, K.; Konečný, J.; Kumar, S.; and McMahan, H. B. 2020 · 2003
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FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling
Chen, C.; Chen, Z.; Zhou, Y.; and Kailkhura, B. 2020 · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
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Parallelized stochastic gradient descent
Zinkevich, M.; Weimer, M.; Li, L.; and Smola, A. J. 2010 · 2010
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Improving network management with software defined networking
Kim, H.; and Feamster, N. 2013 · 2013
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Deep Learning Face Attributes in the Wild
Liu, Z.; Luo, P.; Wang, X.; and Tang, X. 2015 · 2015
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Federated Learning: Strategies for Improving Communication Efficiency
Konecny, J.; McMahan, H. B.; Yu, F. X.; Richtarik, P.; Suresh, A. T.; and Bacon, D. 2016 · 2016
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EMNIST: Extending MNIST to handwritten letters
Cohen, G.; Afshar, S.; Tapson, J.; and Van Schaik, A. 2017 · 2017
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Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, B.; Moore, E.; Ramage, D.; Hampson, S.; and y Arcas, B. A. 2017 · 2017
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Lian, X.; Zhang, C.; Zhang, H.; Hsieh, C.-J.; Zhang, W.; and Liu, J. 2017 · 2017
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Optimization methods for large-scale machine learning
Bottou, L.; Curtis, F. E.; and Nocedal, J. 2018 · 2018
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A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication
Jiang, P.; and Agrawal, G. 2018 · 2018
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Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing
Li, H.; Ota, K.; and Dong, M. 2018 · 2018
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Towards Federated Learning at Scale: System Design
Bonawitz, K.; Eichner, H.; Grieskamp, W.; Huba, D.; Ingerman, A.; Ivanov, V.; Kiddon, C.; Konečnỳ, J.; Mazzocchi, S.; McMahan, H. B.; et al. 2019 · 2019
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LEAF: A Benchmark for Federated Settings
Caldas, S.; Wu, P.; Li, T.; Konecný, J.; McMahan, H. B.; Smith, V.; and Talwalkar, A. 2019 · 2019
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Hierarchical federated learning across heterogeneous cellular networks
Abad, M. S. H.; Ozfatura, E.; Gunduz, D.; and Ercetin, O. 2020 · 2020
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Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach
Han, P.; Wang, S.; and Leung, K. K. 2020 · 2020
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Model Pruning Enables Efficient Federated Learning on Edge Devices
Jiang, Y.; Wang, S.; Valls, V.; Ko, B. J.; Lee, W.-H.; Leung, K. K.; and Tassiulas, L. 2020 · 2020
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SCAFFOLD: Stochastic controlled averaging for federated learning
Karimireddy, S. P.; Kale, S.; Mohri, M.; Reddi, S.; Stich, S.; and Suresh, A. T. 2020 · 2020
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On the Convergence of FedAvg on Non-IID Data
Li, X.; Huang, K.; Yang, W.; Wang, S.; and Zhang, Z. 2020 · 2020
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Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization
Haddadpour, Farzin; et al. 2019 · 2019
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Local SGD Converges Fast and Communicates Little
Stich, S. U. 2019 · 2019
Cited alongside, same era.
Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms
Wang, J.; and Joshi, G. 2019 · 2019
Cited alongside, same era.
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Wang, S.; Tuor, T.; Salonidis, T.; Leung, K. K.; Makaya, C.; He, T.; and Chan, K. 2019 · 2019
Cited alongside, same era.
On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization
Yu, H.; Jin, R.; and Yang, S. 2019 · 2019
Cited alongside, same era.
Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning
Yu, H.; Yang, S.; and Zhu, S. 2019 · 2019
Cited alongside, same era.
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Don’t Use Large Mini-batches, Use Local SGD
Lin, T.; Stich, S. U.; Patel, K. K.; and Jaggi, M. 2020 · 2020
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Client-edge-cloud hierarchical federated learning
Liu, L.; Zhang, J.; Song, S.; and Letaief, K. B. 2020 · 2020
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HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning
Luo, S.; Chen, X.; Wu, Q.; Zhou, Z.; and Yu, S. 2020 · 2020
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D-Cliques: Compensating NonIIDness in Decentralized Federated Learning with Topology
Bellet, A.; Kermarrec, A.-M.; and Lavoie, E. 2021 · 2021
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Multi-Level Local SGD: Distributed SGD for Heterogeneous Hierarchical Networks
Castiglia, T.; Das, A.; and Patterson, S. 2021 · 2021
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