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One underlying assumption of recent federated learning (FL) paradigms is that all local models usually share the same network architecture and size, which becomes impractical for devices with different hardware resources.
Learning multiple layers of features from tiny images
A Krizhevsky · 2009
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Low-rank matrix factorization for deep neural network training with high-dimensional output targets
Tara N Sainath, Brian Kingsbury, Vikas Sindhwani, Ebru Arisoy, and Bhuvana Ramabhadran · 2013
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Restructuring of deep neural network acoustic models with singular value decomposition
Jian Xue, Jinyu Li, and Yifan Gong · 2013
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Speeding-up convolutional neural networks using fine-tuned cp-decomposition
Vadim Lebedev, Yaroslav Ganin, Maksim Rakhuba, Ivan V Oseledets, and Victor S Lempitsky · 2015
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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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Federated learning of deep networks using model averaging
H Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise Aguera y Arcas · 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
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Slimmable neural networks
Jiahui Yu, Linjie Yang, Ning Xu, Jianchao Yang, and Thomas Huang · 2018
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Once-for-all: Train one network and specialize it for efficient deployment
Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, and Song Han · 2019
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Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2019
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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
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FedMD: Heterogenous federated learning via model distillation
Daliang Li and Junpu Wang · 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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Universally slimmable networks and improved training techniques
Jiahui Yu and Thomas S Huang · 2019
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Optimal gradient compression for distributed and federated learning
Alyazeed Albasyoni, Mher Safaryan, Laurent Condat, and Peter Richtárik · 2020
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Heterofl: Computation and communication efficient federated learning for heterogeneous clients
Enmao Diao, Jie Ding, and Vahid Tarokh · 2020
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Stable low-rank tensor decomposition for compression of convolutional neural network
Anh-Huy Phan, Konstantin Sobolev, Konstantin Sozykin, Dmitry Ermilov, Julia Gusak, Petr Tichavskỳ, Valeriy Glukhov, Ivan Oseledets, and Andrzej Cichocki · 2020
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Adaptive federated optimization
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and H Brendan McMahan · 2020
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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
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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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Federated learning based on dynamic regularization
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N Whatmough, and Venkatesh Saligrama · 2021
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Group knowledge transfer: Federated learning of large cnns at the edge
Chaoyang He, Murali Annavaram, and Salman Avestimehr · 2020
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Scaffold: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
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Initialization and regularization of factorized neural layers
Mikhail Khodak, Neil A Tenenholtz, Lester Mackey, and Nicolo Fusi · 2020
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Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2020
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Think locally, act globally: Federated learning with local and global representations
Paul Pu Liang, Terrance Liu, Liu Ziyin, Nicholas B Allen, Randy P Auerbach, David Brent, Ruslan Salakhutdinov, and Louis-Philippe Morency · 2020
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Ensemble distillation for robust model fusion in federated learning
Tao Lin, Lingjing Kong, Sebastian U Stich, and Martin Jaggi · 2020
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Oort: Efficient federated learning via guided participant selection
Fan Lai, Xiangfeng Zhu, Harsha V Madhyastha, and Mosharaf Chowdhury
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Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space
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Communication-efficient federated learning with dual-side low-rank compression
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Fedadapt: Adaptive offloading for iot devices in federated learning
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Data-free knowledge distillation for heterogeneous federated learning
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