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Current federated learning algorithms take tens of communication rounds transmitting unwieldy model weights under ideal circumstances and hundreds when data is poorly distributed.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Overview of the sixth text retrieval conference (trec-6)
Ellen M Voorhees and Donna Harman · 2000
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Pruning training sets for learning of object categories
Anelia Angelova, Yaser Abu-Mostafam, and Pietro Perona · 2005
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Core vector machines: Fast svm training on very large data sets
Ivor W Tsang, James T Kwok, and Pak-Ming Cheung · 2005
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A study of the robustness of knn classifiers trained using soft labels
Neamat El Gayar, Friedhelm Schwenker, and Günther Palm · 2006
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Twitter sentiment classification using distant supervision
Alec Go, Richa Bhayani, and Lei Huang · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 2016
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Practical coreset constructions for machine learning
Olivier Bachem, Mario Lucic, and Andreas Krause · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2017
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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
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Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
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Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization
Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, and Ramtin Pedarsani · 2019
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One-shot federated learning: theoretical limits and algorithms to achieve them
Saber Salehkaleybar, Arsalan Sharifnassab, and S Jamaloddin Golestani · 2019
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Robust and communication-efficient federated learning from non-iid data
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek · 2019
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Order optimal one-shot distributed learning
Arsalan Sharifnassab, Saber Salehkaleybar, and S Jamaloddin Golestani · 2019
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Overcoming forgetting in federated learning on non-iid data
Neta Shoham, Tomer Avidor, Aviv Keren, Nadav Israel, Daniel Benditkis, Liron Mor-Yosef, and Itai Zeitak · 2019
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Yang Chen, Xiaoyan Sun, and Yaochu Jin · 2019
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Neel Guha, Ameet Talwlkar, and Virginia Smith · 2019
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
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Scaffold: Stochastic controlled averaging for on-device federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J Reddi, Sebastian U Stich, and Ananda Theertha Suresh · 2019
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Fedmd: Heterogenous federated learning via model distillation
Daliang Li and Junpu Wang · 2019
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Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2019
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On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2019
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Accelerating federated learning via momentum gradient descent
Wei Liu, Li Chen, Yunfei Chen, and Wenyi Zhang · 2019
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Soft-label dataset distillation and text dataset distillation, 2019
Ilia Sucholutsky and Matthias Schonlau · 2019
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Safa: a semi-asynchronous protocol for fast federated learning with low overhead
Wentai Wu, Ligang He, Weiwei Lin, Stephen Jarvis, et al · 2019
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Asynchronous federated optimization
Cong Xie, Sanmi Koyejo, and Indranil Gupta · 2019
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How to backdoor federated learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov · 2020
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Group knowledge transfer: Federated learning of large cnns at the edge
Chaoyang He, Murali Annavaram, and Salman Avestimehr · 2020
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Adaptive federated optimization
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and H Brendan McMahan · 2020
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Tackling the objective inconsistency problem in heterogeneous federated optimization
Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H Vincent Poor · 2020
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Server averaging for federated learning
George Pu, Yanlin Zhou, Dapeng Wu, and Xiaolin Li · 2021
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