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Federated learning allows distributed devices to collectively train a model without sharing or disclosing the local dataset with a central server.
A stochastic approximation method
Herbert Robbins and Sutton Monro · 1951
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Efficient estimations from a slowly convergent robbins-monro process
David Ruppert · 1988
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New stochastic approximation type procedures
Boris T Polyak · 1990
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Acceleration of stochastic approximation by averaging
Boris T Polyak and Anatoli B Juditsky · 1992
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
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Iterate averaging as regularization for stochastic gradient descent
Gergely Neu and Lorenzo Rosasco · 2018
Cited alongside, same era.
On the convergence of federated optimization in heterogeneous networks
Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, and Virginia Smith · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2019
Cited alongside, same era.
Safa: a semi-asynchronous protocol for fast federated learning with low overhead
Federated learning with quantized global model updates, 2020
Mohammad Mohammadi Amiri, Deniz Gunduz, Sanjeev R. Kulkarni, and H. Vincent Poor · 2020
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Accelerating federated learning via momentum gradient descent
Wei Liu, Li Chen, Yunfei Chen, and Wenyi Zhang · 2020
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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 · 2020
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Federated learning with matched averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni · 2020
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Distilled one-shot federated learning, 2020
Yanlin Zhou, George Pu, Xiyao Ma, Xiaolin Li, and Dapeng Wu · 2020
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Wentai Wu, Ligang He, Weiwei Lin, Stephen Jarvis, et al · 2019
Cited alongside, same era.