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Federated learning is a rapidly-growing area of research which enables a large number of clients to jointly train a machine learning model on privately-held data.
Maximum likelihood estimation of intrinsic dimension
Elizaveta Levina and Peter Bickel · 2005
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Fastfood - computing hilbert space expansions in loglinear time
Quoc V. Le, Tamás Sarlós, and Alexander J. Smola · 2013
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Optimization of convex functions with random pursuit
Sebastian U Stich, Christian L Muller, and Bernd Gartner · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts · 2013
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Danco: An intrinsic dimensionality estimator exploiting angle and norm concentration
Claudio Ceruti, Simone Bassis, Alessandro Rozza, Gabriele Lombardi, Elena Casiraghi, and Paola Campadelli · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Song Han, Huizi Mao, and William J Dally · 2015
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Sparse communication for distributed gradient descent
Alham Fikri Aji and Kenneth Heafield · 2017
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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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Deep gradient compression: Reducing the communication bandwidth for distributed training
Yujun Lin, Song Han, Huizi Mao, Yu Wang, and Bill Dally · 2018
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Measuring the intrinsic dimension of objective landscapes
Chunyuan Li, Heerad Farkhoor, Rosanne Liu, and Jason Yosinski · 2018
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Federated learning: Strategies for improving communication efficiency, 2018
Jakub Konecny, H. Brendan McMahan, Felix X. Yu, Ananda Theertha Suresh, Dave Bacon, and Peter Richtárik · 2018
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Personalizing dialogue agents: I have a dog, do you have pets too?
Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, and Jason Weston · 2018
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Learning compressed transforms with low displacement rank
Anna T Thomas, Albert Gu, Tri Dao, Atri Rudra, and Christopher Re · 2018
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Understanding top-k sparsification in distributed deep learning, 2019
Shaohuai Shi, Xiaowen Chu, Ka Chun Cheung, and Simon See · 2019
Cited alongside, same era.
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization
Thijs Vogels, Sai Praneeth Karimireddy, and Martin Jaggi · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Cited alongside, same era.
Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
Cited alongside, same era.
On the intrinsic dimensionality of image representations
Sixue Gong, Vishnu Naresh Boddeti, and Anil K Jain · 2019
Cited alongside, same era.
Parameter-efficient transfer learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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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Dba: Distributed backdoor attacks against federated learning
Chulin Xie, Keli Huang, Pin-Yu Chen, and Bo Li · 2020
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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
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On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2020
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Federated learning with only positive labels, 2020
Felix X. Yu, Ankit Singh Rawat, Aditya Krishna Menon, and Sanjiv Kumar · 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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Cited alongside, same era.
Analyzing federated learning through an adversarial lens
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo · 2019
Cited alongside, same era.
Agnostic federated learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
Cited alongside, same era.
Robust and communication-efficient federated learning from non-i.i.d. data
Felix Sattler, Simon Wiedemann, K. Müller, and W. Samek · 2020
Cited alongside, same era.
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
Cited alongside, same era.
The error-feedback framework: Better rates for sgd with delayed gradients and compressed updates
Sebastian U Stich and Sai Praneeth Karimireddy · 2020
Cited alongside, same era.
Optimal gradient compression for distributed and federated learning
Alyazeed Albasyoni, Mher Safaryan, Laurent Condat, and Peter Richtárik · 2020
Cited alongside, same era.
Later among the works it cites.
Acceleration for compressed gradient descent in distributed and federated optimization
Zhize Li, Dmitry Kovalev, Xun Qian, and Peter Richtarik · 2020
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Fedml: A research library and benchmark for federated machine learning
Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, and Salman Avestimehr · 2020
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Intrinsic dimensionality explains the effectiveness of language model fine-tuning
Armen Aghajanyan, Sonal Gupta, and Luke Zettlemoyer · 2021
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EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback
Peter Richtárik, Igor Sokolov, and Ilyas Fatkhullin · 2021
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The intrinsic dimension of images and its impact on learning
Phil Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, and Tom Goldstein · 2021
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Parameter-efficient transfer learning with diff pruning
Demi Guo, Alexander M Rush, and Yoon Kim · 2021
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Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
Elad Ravfogel, Shauli Ben-Zaken, and Yoav Goldberg · 2021
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