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Federated learning (FL) is a popular technique to train machine learning (ML) models with decentralized data.
Methods of Information Geometry
Shun-ichi Amari and Hiroshi Nagaoka · 2000
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Learning Multiple Layers of Features from Tiny Images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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New Insights and Perspectives on the Natural Gradient Method
James Martens · 2014
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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
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
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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Distributed Mean Estimation with Limited Communication
Ananda Theertha Suresh, X Yu Felix, Sanjiv Kumar, and H Brendan McMahan · 2017
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Three Factors Influencing Minima in SGD
Stanisław Jastrzębski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer, Yoshua Bengio, and Amos Storkey · 2017
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Federated Multi-Task Learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet Talwalkar · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Differentially Private Federated Learning: A Client Level Perspective
Robin C Geyer, Tassilo Klein, and Moin Nabi · 2017
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Deep Models under the GAN: Information Leakage from Collaborative Deep Learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Perez-Cruz · 2017
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Applied Federated Learning: Improving Google Keyboard Query Suggestions
Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, and Françoise Beaufays · 2018
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Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
Sebastian Caldas, Jakub Konečny, H Brendan McMahan, and Ameet Talwalkar · 2018
Cited alongside, same era.
Don’t Decay the Learning Rate, Increase the Batch Size
Samuel L Smith, Pieter-Jan Kindermans, Chris Ying, and Quoc V Le · 2018
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, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
Cited alongside, same era.
ELFISH: Resource-Aware Federated Learning on Heterogeneous Edge Devices
Zirui Xu, Zhao Yang, Jinjun Xiong, Jianlei Yang, and Xiang Chen · 2019
Cited alongside, same era.
Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-update SGD
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
Later among the works it cites.
Optimizing Federated Learning on Non-IID Data With Reinforcement Learning
Hao Wang, Zakhary Kaplan, Di Niu, and Baochun Li · 2020
Later among the works it cites.
Federated Optimization in Heterogeneous Networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
Later among the works it cites.
FetchSGD: Communication-Efficient Federated Learning with Sketching
Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, and Raman Arora · 2020
Later among the works it cites.
On the Convergence of FedAvg on Non-IID Data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2020
Later among the works it cites.
Federated Learning for Vision-and-Language Grounding Problems
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Jianyu Wang and Gauri Joshi · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Critical Learning Periods in Deep Networks
Alessandro Achille, Matteo Rovere, and Stefano Soatto · 2019
Cited alongside, same era.
On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length
Stanislaw Jastrzebski, Zachary Kenton, Nicolas Ballas, Asja Fischer, Yoshua Bengio, and Amos J Storkey · 2019
Cited alongside, same era.
Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence
Aditya Sharad Golatkar, Alessandro Achille, and Stefano Soatto · 2019
Cited alongside, same era.
Exploiting Unintended Feature Leakage in Collaborative Learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 2019
Cited alongside, same era.
Deep Leakage from Gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
Cited alongside, same era.
Agnostic Federated Learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
Cited alongside, same era.
Fenglin Liu, Xian Wu, Shen Ge, Wei Fan, and Yuexian Zou · 2020
Later among the works it cites.
Federated Learning with Matched Averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni · 2020
Later among the works it cites.
A Survey on Federated Learning for Resource-Constrained IoT Devices
Ahmed Imteaj, Urmish Thakker, Shiqiang Wang, Jian Li, and M Hadi Amini · 2021
Closest in time.
Oort: Efficient Federated Learning via Guided Participant Selection
Fan Lai, Xiangfeng Zhu, Harsha V Madhyastha, and Mosharaf Chowdhury · 2021
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Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers
Guojun Xiong, Gang Yan, and Jian Li · 2021
Closest in time.
Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization
Stanislaw Jastrzebski, Devansh Arpit, Oliver Astrand, Giancarlo B Kerg, Huan Wang, Caiming Xiong, Richard Socher, Kyunghyun Cho, and Krzysztof J Geras · 2021
Closest in time.
ACCORDION: Adaptive Gradient Communication via Critical Learning Regime Identification
Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, and Dimitris Papailiopoulos · 2021
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