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Federated learning (FL) has attracted growing attention via data-private collaborative training on decentralized clients.
Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Tiny imagenet visual recognition challenge
Pouransari Pouransari and Saman Ghili · 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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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2016
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Federated learning of deep networks using model averaging
H. Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise Agüera y Arcas · 2016
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2017
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H. Lampert · 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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Incremental learning of object detectors without catastrophic forgetting
Konstantin Shmelkov, Cordelia Schmid, and Karteek Alahari · 2017
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Dïot: A crowdsourced self-learning approach for detecting compromised iot devices
Thien Duc Nguyen, Samuel Marchal, Markus Miettinen, Minh Hoang Dang, N. Asokan, and Ahmad-Reza Sadeghi · 2018
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On the convergence of federated optimization in heterogeneous networks
Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, and Virginia Smith · 2018
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Memory replay gans: Learning to generate new categories without forgetting
Chenshen Wu, Luis Herranz, Xialei Liu, yaxing wang, Joost van de Weijer, and Bogdan Raducanu · 2018
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Two-stage label embedding via neural factorization machine for multi-label classification
Chen Chen, Haobo Wang, Weiwei Liu, Xingyuan Zhao, Tianlei Hu, and Gang Chen · 2019
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Federated learning for emoji prediction in a mobile keyboard
Swaroop Ramaswamy, Rajiv Mathews, Kanishka Rao, and Françoise Beaufays · 2019
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Overcoming forgetting in federated learning on non-iid data
Neta Shoham, Tomer Avidor, Aviv Keren, Nadav Israel, Daniel Benditkis, Liron Mor-Yosef, and Itai Zeitak · 2019
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Eavesdrop the composition proportion of training labels in federated learning
Lixu Wang, Shichao Xu, Xiao Wang, and Qi Zhu · 2019
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Large scale incremental learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, and Yun Fu · 2019
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Bayesian nonparametric federated learning of neural networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, and Yasaman Khazaeni · 2019
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Communication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregation
Yang Chen, Xiaoyan Sun, and Yaochu Jin · 2020
Cited alongside, same era.
What can be transferred: Unsupervised domain adaptation for endoscopic lesions segmentation
Jiahua Dong, Yang Cong, Gan Sun, Bineng Zhong, and Xiaowei Xu · 2020
Cited alongside, same era.
Podnet: Pooled outputs distillation for small-tasks incremental learning
Arthur Douillard, Matthieu Cord, Charles Ollion, Thomas Robert, and Eduardo Valle · 2020
Cited alongside, same era.
Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2020
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.
Where and how to transfer: Knowledge aggregation-induced transferability perception for unsupervised domain adaptation
Jiahua Dong, Yang Cong, Gan Sun, Zhen Fang, and Zhengming Ding · 2021
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Learning bounds for open-set learning
Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, and Guangquan Zhang · 2021
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Federated adversarial debiasing for fair and transferable representations
Junyuan Hong, Zhuangdi Zhu, Shuyang Yu, Zhangyang Wang, Hiroko H Dodge, and Jiayu Zhou · 2021
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Distilling causal effect of data in class-incremental learning
Xinting Hu, Kaihua Tang, Chunyan Miao, Xian-Sheng Hua, and Hanwang Zhang · 2021
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Split-and-bridge: Adaptable class incremental learning within a single neural network
Jong-Yeong Kim and Dong-Wan Choi · 2021
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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
Cited alongside, same era.
Unsupervised model personalization while preserving privacy and scalability: An open problem
Matthias De Lange, Xu Jia, Sarah Parisot, Ales Leonardis, Gregory Slabaugh, and Tinne Tuytelaars · 2020
Cited alongside, same era.
Learning deep kernels for non-parametric two-sample tests
Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, and Danica J. Sutherland · 2020
Cited alongside, same era.
Mnemonics training: Multi-class incremental learning without forgetting
Yaoyao Liu, Yuting Su, An-An Liu, Bernt Schiele, and Qianru Sun · 2020
Cited alongside, same era.
Federated adversarial domain adaptation
Xingchao Peng, Zijun Huang, Yizhe Zhu, and Kate Saenko · 2020
Cited alongside, same era.
Distributed federated learning for ultra-reliable low-latency vehicular communications
Sumudu Samarakoon, Mehdi Bennis, Walid Saad, and Mérouane Debbah · 2020
Cited alongside, same era.
Federated learning with matched averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris S. Papailiopoulos, and Yasaman Khazaeni · 2020
Cited alongside, same era.
Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, and Qi Dou · 2021
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Bapa-net: Boundary adaptation and prototype alignment for cross-domain semantic segmentation
Yahao Liu, Jihong Deng, Xinchen Gao, Wen Li, and Lixin Duan · 2021
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Adaptive aggregation networks for class-incremental learning
Yaoyao Liu, Bernt Schiele, and Qianru Sun · 2021
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A novel attribute reconstruction attack in federated learning
Lingjuan Lyu and Chen Chen · 2021
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On learning the geodesic path for incremental learning
Christian Simon, Piotr Koniusz, and Mehrtash Harandi · 2021
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Addressing class imbalance in federated learning
Lixu Wang, Shichao Xu, Xiao Wang, and Qi Zhu · 2021
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Der: Dynamically expandable representation for class incremental learning
Shipeng Yan, Jiangwei Xie, and Xuming He · 2021
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Achieving linear speedup with partial worker participation in non-iid federated learning
Haibo Yang, Minghong Fang, and Jia Liu · 2021
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FLOP: federated learning on medical datasets using partial networks
Qian Yang, Jianyi Zhang, Weituo Hao, Gregory Spell, and Lawrence Carin · 2021
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DATA: differentiable architecture approximation with distribution guided sampling
Xinbang Zhang, Jianlong Chang, Yiwen Guo, Gaofeng Meng, Shiming Xiang, Zhouchen Lin, and Chunhong Pan · 2021
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You only search once: Single shot neural architecture search via direct sparse optimization
Xinbang Zhang, Zehao Huang, Naiyan Wang, Shiming Xiang, and Chunhong Pan · 2021
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Denoised maximum classifier discrepancy for source-free unsupervised domain adaptation
Tong Chu, Yahao Liu, Jinhong Deng, Wen Li, and Lixin Duan · 2022
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Undoing the damage of label shift for cross-domain semantic segmentation
Yahao Liu, Jinhong Deng, Jiale Tao, Tong Chu, Lixin Duan, and Wen Li · 2022
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Non-transferable learning: A new approach for model ownership verification and applicability authorization
Lixu Wang, Shichao Xu, Ruiqi Xu, Xiao Wang, and Qi Zhu · 2022
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