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Federated learning has emerged as an important distributed learning paradigm, where a server aggregates a global model from many client-trained models while having no access to the client data.
Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem
Michael McCloskey and Neal J Cohen · 1989
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Gradient-based Learning Applied to Document Recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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The unreasonable effectiveness of data
Alon Halevy, Peter Norvig, and Fernando Pereira · 2009
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Learning Multiple Layers of Features from Tiny Images
Alex Krizhevsky · 2009
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Face recognition performance: Role of demographic information
Brendan F Klare, Mark J Burge, Joshua C Klontz, Richard W Vorder Bruegge, and Anil K Jain · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Lecture notes on information theory
Ganesh Ajjanagadde, Anuran Makur, Jason Klusowski, Sheng Xu, et al · 2017
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Differentially private federated learning: A client level perspective
Robin C Geyer, Tassilo Klein, and Moin Nabi · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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Communication-efficient Learning of Deep Networks from Decentralized Data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 2017
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Continual Learning with Deep Generative Replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
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Federated multi-task learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Fairness constraints: Mechanisms for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P Gummadi · 2017
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Universal neural machine translation for extremely low resource languages
Jiatao Gu, Hany Hassan, Jacob Devlin, and Victor OK Li · 2018
Cited alongside, same era.
Eunjeong Jeong, Seungeun Oh, Hyesung Kim, Jihong Park, Mehdi Bennis, and Seong-Lyun Kim · 2018
Cited alongside, same era.
Gender bias in artificial intelligence: The need for diversity and gender theory in machine learning
A hybrid approach to privacy-preserving federated learning
Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, and Yi Zhou · 2019
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Beyond inferring class representatives: User-level privacy leakage from federated learning
Zhibo Wang, Mengkai Song, Zhifei Zhang, Yang Song, Qian Wang, and Hairong Qi · 2019
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Does face recognition accuracy get better with age? deep face matchers say no
Vítor Albiero, Kevin Bowyer, Kushal Vangara, and Michael King · 2020
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Analysis of gender inequality in face recognition accuracy
Vítor Albiero, Krishnapriya KS, Kushal Vangara, Kai Zhang, Michael C King, and Kevin W Bowyer · 2020
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Zeroq: A novel zero shot quantization framework
Yaohui Cai, Zhewei Yao, Zhen Dong, Amir Gholami, Michael W Mahoney, and Kurt Keutzer · 2020
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Susan Leavy · 2018
Cited alongside, same era.
Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2018
Cited alongside, same era.
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
Cited alongside, same era.
mixup: Beyond Empirical Risk Minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
Federated Learning with Non-IID Data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
Cited alongside, same era.
Federated learning with personalization layers
Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary · 2019
Cited alongside, same era.
Variational Federated Multi-Task Learning
Luca Corinzia and Joachim M Buhmann · 2019
Cited alongside, same era.
Fair resource allocation in federated learning
Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith · 2019
Cited alongside, same era.
Jack Goetz and Ambuj Tewari · 2020
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WAFFLe: Weight Anonymized Factorization for Federated Learning
Weituo Hao, Nikhil Mehta, Kevin J Liang, Pengyu Cheng, Mostafa El-Khamy, and Lawrence Carin · 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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On safeguarding privacy and security in the framework of federated learning
Chuan Ma, Jun Li, Ming Ding, Howard H Yang, Feng Shu, Tony QS Quek, and H Vincent Poor · 2020
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Federated learning with differential privacy: Algorithms and performance analysis
Kang Wei, Jun Li, Ming Ding, Chuan Ma, Howard H Yang, Farhad Farokhi, Shi Jin, Tony QS Quek, and H Vincent Poor · 2020
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Dreaming to distill: Data-free knowledge transfer via deepinversion
Hongxu Yin, Pavlo Molchanov, Jose M Alvarez, Zhizhong Li, Arun Mallya, Derek Hoiem, Niraj K Jha, and Jan Kautz · 2020
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Exploring racial bias within face recognition via per-subject adversarially-enabled data augmentation
Seyma Yucer, Samet Akçay, Noura Al-Moubayed, and Toby P Breckon · 2020
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MixKD: Towards Efficient Distillation of Large-scale Language Models
Kevin J Liang, Weituo Hao, Dinghan Shen, Yufan Zhou, Weizhu Chen, Changyou Chen, and Lawrence Carin · 2021
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