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Federated learning is a distributed framework according to which a model is trained over a set of devices, while keeping data localized.
Codedreduce: A fast and robust framework for gradient aggregation in distributed learning
Amirhossein Reisizadeh, Saurav Prakash, Ramtin Pedarsani, and Amir Salman Avestimehr · 1902
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On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 1907
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Robust and communication-efficient collaborative learning
Amirhossein Reisizadeh, Hossein Taheri, Aryan Mokhtari, Hamed Hassani, and Ramtin Pedarsani · 1907
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Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 1908
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Some np-complete problems in quadratic and nonlinear programming
Katta G Murty and Santosh N Kabadi · 1987
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The tradeoffs of large scale learning
Léon Bottou and Olivier Bousquet · 2008
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Privacy aware learning
John C Duchi, Michael I Jordan, and Martin J Wainwright · 2014
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1-bit stochastic gradient descent and its application to data-parallel distributed training of speech dnns
Frank Seide, Hao Fu, Jasha Droppo, Gang Li, and Dong Yu · 2014
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Short-dot: Computing large linear transforms distributedly using coded short dot products
Sanghamitra Dutta, Viveck Cadambe, and Pulkit Grover · 2016
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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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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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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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Rashish Tandon, Qi Lei, Alexandros G Dimakis, and Nikos Karampatziakis · 2016
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Qsgd: Communication-efficient sgd via gradient quantization and encoding
Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic · 2017
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Distributed statistical machine learning in adversarial settings: Byzantine gradient descent
Yudong Chen, Lili Su, and Jiaming Xu · 2017
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Speeding up distributed machine learning using codes
Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris Papailiopoulos, and Kannan Ramchandran · 2017
Cited alongside, same era.
Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 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.
Polynomial codes: an optimal design for high-dimensional coded matrix multiplication
Qian Yu, Mohammad Ali Maddah-Ali, and A Salman Avestimehr · 2017
Cited alongside, same era.
cpsgd: Communication-efficient and differentially-private distributed sgd
Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, and Brendan McMahan · 2018
Cited alongside, same era.
Distributed federated learning for ultra-reliable low-latency vehicular communications
Sumudu Samarakoon, Mehdi Bennis, Walid Saady, and Merouane Debbah · 2018
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Local sgd converges fast and communicates little
Sebastian U Stich · 2018
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Jianyu Wang and Gauri Joshi · 2018
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Byzantine-robust distributed learning: Towards optimal statistical rates
Dong Yin, Yudong Chen, Kannan Ramchandran, and Peter Bartlett · 2018
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Compressed distributed gradient descent: Communication-efficient consensus over networks
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Balancing communication and computation in distributed optimization
Albert Berahas, Raghu Bollapragada, Nitish Shirish Keskar, and Ermin Wei · 2018
Cited alongside, same era.
signsgd: Compressed optimisation for non-convex problems
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, and Anima Anandkumar · 2018
Cited alongside, same era.
Accelerating the convergence rates of distributed subgradient methods with adaptive quantization
Thinh T Doan, Siva Theja Maguluri, and Justin Romberg · 2018
Cited alongside, same era.
Loadaboost: Loss-based adaboost federated machine learning on medical data
Li Huang, Yifeng Yin, Zeng Fu, Shifa Zhang, Hao Deng, and Dianbo Liu · 2018
Cited alongside, same era.
Speeding up distributed machine learning using codes
Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris Papailiopoulos, and Kannan Ramchandran · 2018
Cited alongside, same era.
Don’t use large mini-batches, use local sgd
Tao Lin, Sebastian U Stich, Kumar Kshitij Patel, and Martin Jaggi · 2018
Cited alongside, same era.
The landscape of empirical risk for nonconvex losses
Song Mei, Yu Bai, Andrea Montanari, et al · 2018
Cited alongside, same era.
Xin Zhang, Jia Liu, Zhengyuan Zhu, and Elizabeth S Bentley · 2018
Later among the works it cites.
Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, H Brendan McMahan, et al · 2019
Closest in time.
Robust federated learning in a heterogeneous environment
Avishek Ghosh, Justin Hong, Dong Yin, and Kannan Ramchandran · 2019
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Neel Guha, Ameet Talwlkar, and Virginia Smith · 2019
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Decentralized stochastic optimization and gossip algorithms with compressed communication
Anastasia Koloskova, Sebastian U Stich, and Martin Jaggi · 2019
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Peer-to-peer federated learning on graphs
Anusha Lalitha, Osman Cihan Kilinc, Tara Javidi, and Farinaz Koushanfar · 2019
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Federated learning: Collaborative machine learning without centralized training data
Brendan McMahan and Daniel Ramage · 2019
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Matcha: Speeding up decentralized sgd via matching decomposition sampling
Jianyu Wang, Anit Kumar Sahu, Zhouyi Yang, Gauri Joshi, and Soummya Kar · 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 heavy hitters discovery with differential privacy
Wennan Zhu, Peter Kairouz, Haicheng Sun, Brendan McMahan, and Wei Li · 2019
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