Fetching the paper…
Reading the bibliography…
It is well understood that client-master communication can be a primary bottleneck in Federated Learning.
Television by pulse code modulation
WM Goodall · 1951
Earlier work this paper cites.
Picture coding using pseudo-random noise
Lawrence Roberts · 1962
Earlier work this paper cites.
Secure multi-party computation problems and their applications: a review and open problems
Wenliang Du and Mikhail J Atallah · 2001
Earlier work this paper cites.
Fast kernel classifiers with online and active learning
Antoine Bordes, Seyda Ertekin, Jason Weston, and Léon Bottou · 2005
Earlier work this paper cites.
Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
Earlier work this paper cites.
Multi-core for mobile phones
CH Van Berkel · 2009
Earlier work this paper cites.
Efficiency of coordinate descent methods on huge-scale optimization problems
Yu Nesterov · 2012
Earlier work this paper cites.
An in-depth study of LTE: Effect of network protocol and application behavior on performance
Junxian Huang, Feng Qian, Yihua Guo, Yuanyuan Zhou, Qiang Xu, Z Morley Mao, Subhabrata Sen, and Oliver Spatscheck · 2013
Earlier work this paper cites.
An accelerated proximal coordinate gradient method
Qihang Lin, Zhaosong Lu, and Lin Xiao · 2014
Earlier work this paper cites.
Stochastic gradient descent, weighted sampling, and the randomized kaczmarz algorithm
Deanna Needell, Rachel Ward, and Nati Srebro · 2014
Earlier work this paper cites.
Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function
Peter Richtárik and Martin Takáč · 2014
Earlier work this paper cites.
Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization
Shai Shalev-Shwartz and Tong Zhang · 2014
Earlier work this paper cites.
Accelerated, parallel, and proximal coordinate descent
Olivier Fercoq and Peter Richtárik · 2015
Earlier work this paper cites.
A comprehensive comparison of multiparty secure additions with differential privacy
Slawomir Goryczka and Li Xiong · 2015
Earlier work this paper cites.
Online batch selection for faster training of neural networks
Ilya Loshchilov and Frank Hutter · 2015
Earlier work this paper cites.
Quartz: Randomized dual coordinate ascent with arbitrary sampling
Zheng Qu, Peter Richtárik, and Tong Zhang · 2015
Earlier work this paper cites.
Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2015
Earlier work this paper cites.
Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
Earlier work this paper cites.
Discriminative learning of deep convolutional feature point descriptors
Edgar Simo-Serra, Eduard Trulls, Luis Ferraz, Iasonas Kokkinos, Pascal Fua, and Francesc Moreno-Noguer · 2015
Earlier work this paper cites.
Stochastic optimization with importance sampling for regularized loss minimization
Peilin Zhao and Tong Zhang · 2015
Earlier work this paper cites.
Even faster accelerated coordinate descent using non-uniform sampling
Zeyuan Allen-Zhu, Zheng Qu, Peter Richtárik, and Yang Yuan · 2016
Cited alongside, same era.
Linear convergence of gradient and proximal-gradient methods under the polyak-łojasiewicz condition
Hamed Karimi, Julie Nutini, and Mark Schmidt · 2016
Cited alongside, same era.
Qsgd: Communication-efficient sgd via gradient quantization and encoding
Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic · 2017
Cited alongside, same era.
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.
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.
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
Later among the works it cites.
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
Later among the works it cites.
99% of parallel optimization is inevitably a waste of time
Konstantin Mishchenko, Filip Hanzely, and Peter Richtárik · 2019
Later among the works it cites.
NUQSGD: Improved communication efficiency for data-parallel SGD via nonuniform quantization
Ali Ramezani-Kebrya, Fartash Faghri, and Daniel M Roy · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Sebastian U Stich, Anant Raj, and Martin Jaggi · 2017
Cited alongside, same era.
Terngrad: Ternary gradients to reduce communication in distributed deep learning
Wei Wen, Cong Xu, Feng Yan, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2017
Cited alongside, same era.
Zipml: Training linear models with end-to-end low precision, and a little bit of deep learning
Hantian Zhang, Jerry Li, Kaan Kara, Dan Alistarh, Ji Liu, and Ce Zhang · 2017
Cited alongside, same era.
Optimization methods for large-scale machine learning
Léon Bottou, Frank E Curtis, and Jorge Nocedal · 2018
Cited alongside, same era.
Leaf: A benchmark for federated settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
Cited alongside, same era.
Not all samples are created equal: Deep learning with importance sampling
Angelos Katharopoulos and François Fleuret · 2018
Cited alongside, same era.
Randomized distributed mean estimation: Accuracy vs. communication
Jakub Konečný and Peter Richtárik · 2018
Cited alongside, same era.
Local SGD converges fast and communicates little
Sebastian U Stich · 2019
Later among the works it cites.
PowerSGD: Practical low-rank gradient compression for distributed optimization
Thijs Vogels, Sai Praneeth Karimireddy, and Martin Jaggi · 2019
Later among the works it cites.
Yae Jee Cho, Jianyu Wang, and Gauri Joshi · 2020
Closest in time.
Federated learning of a mixture of global and local models
Filip Hanzely and Peter Richtárik · 2020
Closest in time.
A better alternative to error feedback for communication-efficient distributed learning
Samuel Horváth and Peter Richtárik · 2020
Closest in time.
Tighter theory for local SGD on identical and heterogeneous data
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik · 2020
Closest in time.
Fast-convergent federated learning
Hung T Nguyen, Vikash Sehwag, Seyyedali Hosseinalipour, Christopher G Brinton, Mung Chiang, and H Vincent Poor · 2020
Closest in time.
Communication-efficient federated learning via optimal client sampling
Monica Ribero and Haris Vikalo · 2020
Closest in time.
The error-feedback framework: Better rates for SGD with delayed gradients and compressed communication
Sebastian U Stich and Sai Praneeth Karimireddy · 2020
Closest in time.
Oort: Efficient federated learning via guided participant selection
Fan Lai, Xiangfeng Zhu, Harsha V Madhyastha, and Mosharaf Chowdhury · 2021
Closest in time.
Tilted empirical risk minimization
Tian Li, Ahmad Beirami, Maziar Sanjabi, and Virginia Smith · 2021
Closest in time.
Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning
Jinhyun So, Başak Güler, and A Salman Avestimehr · 2021
Closest in time.
Achieving linear speedup with partial worker participation in non-iid federated learning
Haibo Yang, Minghong Fang, and Jia Liu · 2021
Closest in time.
Papaya: Practical, private, and scalable federated learning
Dzmitry Huba, John Nguyen, Kshitiz Malik, Ruiyu Zhu, Mike Rabbat, Ashkan Yousefpour, Carole-Jean Wu, Hongyuan Zhan, Pavel Ustinov, Harish Srinivas, et al · 2022
Closest in time.
Tackling system and statistical heterogeneity for federated learning with adaptive client sampling
Bing Luo, Wenli Xiao, Shiqiang Wang, Jianwei Huang, and Leandros Tassiulas · 2022
Closest in time.