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Federated learning (FL) is an important technique for learning models from decentralized data in a privacy-preserving way.
Inequalities for the rth absolute moment of a sum of random variables, 1 ≤ \leq r ≤ \leq 2
Bengt von Bahr and Carl-Gustav Esseen · 1965
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Convolutional neural networks for sentence classification
Yoon Kim · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Image-based recommendations on styles and substitutes
Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Emnist: Extending mnist to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and Andre Van Schaik · 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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Explaining the privacy paradox: A systematic review of literature investigating privacy attitude and behavior
Nina Gerber, Paul Gerber, and Melanie Volkamer · 2018
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Adaptive methods for nonconvex optimization
S Reddi, Manzil Zaheer, Devendra Sachan, Satyen Kale, and Sanjiv Kumar · 2018
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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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On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2019
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Adagrad—an optimizer for stochastic gradient descent
Agnes Lydia and Sagayaraj Francis · 2019
Cited alongside, same era.
Fedboost: A communication-efficient algorithm for federated learning
Jenny Hamer, Mehryar Mohri, and Ananda Theertha Suresh · 2020
Cited alongside, same era.
Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2020
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Privacy and robustness in federated learning: Attacks and defenses
Lingjuan Lyu, Han Yu, Xingjun Ma, Lichao Sun, Jun Zhao, Qiang Yang, and Philip S Yu · 2020
Cited alongside, same era.
Adaptive federated optimization
Sashank J Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and Hugh Brendan McMahan · 2020
Cited alongside, same era.
Fetchsgd: Communication-efficient federated learning with sketching
Federated learning for predicting clinical outcomes in patients with covid-19
Ittai Dayan, Holger R Roth, Aoxiao Zhong, Ahmed Harouni, Amilcare Gentili, Anas Z Abidin, Andrew Liu, Anthony Beardsworth Costa, Bradford J Wood, Chien-Sung Tsai, et al · 2021
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Yann Fraboni, Richard Vidal, Laetitia Kameni, and Marco Lorenzi · 2021
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On the impact of client sampling on federated learning convergence
Yann Fraboni, Richard Vidal, Laetitia Kameni, and Marco Lorenzi · 2021
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Fl-ntk: A neural tangent kernel-based framework for federated learning analysis
Baihe Huang, Xiaoxiao Li, Zhao Song, and Xin Yang · 2021
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Fedspeech: Federated text-to-speech with continual learning
Ziyue Jiang, Yi Ren, Ming Lei, and Zhou Zhao · 2021
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Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, and Raman Arora · 2020
Cited alongside, same era.
Tackling the objective inconsistency problem in heterogeneous federated optimization
Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H Vincent Poor · 2020
Cited alongside, same era.
Mind: A large-scale dataset for news recommendation
Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, et al · 2020
Cited alongside, same era.
Personalized federated learning with gaussian processes
Idan Achituve, Aviv Shamsian, Aviv Navon, Gal Chechik, and Ethan Fetaya · 2021
Cited alongside, same era.
Federated learning under arbitrary communication patterns
Dmitrii Avdiukhin and Shiva Kasiviswanathan · 2021
Cited alongside, same era.
Advancing covid-19 diagnosis with privacy-preserving collaboration in artificial intelligence
Xiang Bai, Hanchen Wang, Liya Ma, Yongchao Xu, Jiefeng Gan, Ziwei Fan, Fan Yang, Ke Ma, Jiehua Yang, Song Bai, et al · 2021
Cited alongside, same era.
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Highly accurate protein structure prediction with alphafold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al · 2021
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Fedrec++: Lossless federated recommendation with explicit feedback
Feng Liang, Weike Pan, and Zhong Ming · 2021
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Ldp-fl: Practical private aggregation in federated learning with local differential privacy
Lichao Sun, Jianwei Qian, and Xun Chen · 2021
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Federated learning with fair averaging
Zheng Wang, Xiaoliang Fan, Jianzhong Qi, Chenglu Wen, Cheng Wang, and Rongshan Yu · 2021
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Federated continual learning with weighted inter-client transfer
Jaehong Yoon, Wonyong Jeong, Giwoong Lee, Eunho Yang, and Sung Ju Hwang · 2021
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Federated composite optimization
Honglin Yuan, Manzil Zaheer, and Sashank Reddi · 2021
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