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Federated learning enables a large amount of edge computing devices to jointly learn a model without data sharing.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
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Distributed optimization: algorithms and convergence rates
Dusan Jakovetic · 2013
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Introductory lectures on convex optimization: a basic course , volume 87
Yurii Nesterov · 2013
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Communication-efficient algorithms for statistical optimization
Yuchen Zhang, John C. Duchi, and Martin J. Wainwright · 2013
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Communication-efficient distributed optimization using an approximate Newton-type method
Ohad Shamir, Nati Srebro, and Tong Zhang · 2014
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Iterative parameter mixing for distributed large-margin training of structured predictors for natural language processing
Gregory Francis Coppola · 2015
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Federated optimization: distributed optimization beyond the datacenter
Jakub Kone v · 2015
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
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DiSCO: distributed optimization for self-concordant empirical loss
Yuchen Zhang and Xiao Lin · 2015
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Federated learning: strategies for improving communication efficiency
Jakub Kone v · 2016
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MLlib: machine learning in Apache Spark
Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, and Sean Owen · 2016
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AIDE: fast and communication efficient distributed optimization
Sashank J Reddi, Jakub Konecnỳ, Peter Richtárik, Barnabás Póczós, and Alex Smola · 2016
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Distributed coordinate descent method for learning with big data
Peter Richtárik and Martin Takác · 2016
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CoCoA: A general framework for communication-efficient distributed optimization
Virginia Smith, Simone Forte, Chenxin Ma, Martin Takac, Michael I Jordan, and Martin Jaggi · 2016
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A general distributed dual coordinate optimization framework for regularized loss minimization
Shun Zheng, Fen Xia, Wei Xu, and Tong Zhang · 2016
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Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
Cited alongside, same era.
Differentially private federated learning: A client level perspective
Robin C Geyer, Tassilo Klein, Moin Nabi, and SAP SE · 2017
Cited alongside, same era.
Deep models under the GAN: information leakage from collaborative deep learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Pérez-Cruz · 2017
Cited alongside, same era.
Stochastic, distributed and federated optimization for machine learning
Jakub Kone v · 2017
Cited alongside, same era.
Communication-efficient sparse regression
Jason D Lee, Qiang Liu, Yuekai Sun, and Jonathan E Taylor · 2017
Cited alongside, same era.
Local SGD converges fast and communicates little
Sebastian U Stich · 2018
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Sparsified SGD with memory
Sebastian U Stich, Jean-Baptiste Cordonnier, and Martin Jaggi · 2018
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Jianyu Wang and Gauri Joshi · 2018
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Graph oracle models, lower bounds, and gaps for parallel stochastic optimization
Blake E Woodworth, Jialei Wang, Adam Smith, Brendan McMahan, and Nati Srebro · 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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Distributed learning with regularized least squares
Shao-Bo Lin, Xin Guo, and Ding-Xuan Zhou · 2017
Cited alongside, same era.
Communication-Efficient Learning of Deep Networks from Decentralized Data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Cited alongside, same era.
Federated multi-task learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar · 2017
Cited alongside, same era.
Fan Zhou and Guojing Cong · 2017
Cited alongside, same era.
How to backdoor federated learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov · 2018
Cited alongside, same era.
Gradient primal-dual algorithm converges to second-order stationary solution for nonconvex distributed optimization over networks
Mingyi Hong, Meisam Razaviyayn, and Jason Lee · 2018
Cited alongside, same era.
Don’t use large mini-batches, use local sgd
Tao Lin, Sebastian U Stich, and Martin Jaggi · 2018
Cited alongside, same era.
First analysis of local gd on heterogeneous data
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik · 2019
Closest in time.
Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2019
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Exploiting unintended feature leakage in collaborative learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 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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Adaptive federated learning in resource constrained edge computing systems
Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K Leung, Christian Makaya, Ting He, and Kevin Chan · 2019
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A sharper generalization bound for divide-and-conquer ridge regression
Shusen Wang · 2019
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Asynchronous federated optimization
Cong Xie, Sanmi Koyejo, and Indranil Gupta · 2019
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Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning
Hao Yu, Sen Yang, and Shenghuo Zhu · 2019
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Federated reinforcement learning
Hankz Hankui Zhuo, Wenfeng Feng, Qian Xu, Qiang Yang, and Yufeng Lin · 2019
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