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Federated learning (FL) is a hot collaborative training framework via aggregating model parameters of decentralized local 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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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, koray kavukcuoglu, and Daan Wierstra · 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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Few-shot class-incremental learning by sampling multi-phase tasks
Da-Wei Zhou, Han-Jia Ye, Liang Ma, Di Xie, Shiliang Pu, and De-Chuan Zhan · 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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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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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
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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.
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.
Federated adversarial domain adaptation
Xingchao Peng, Zijun Huang, Yizhe Zhu, and Kate Saenko · 2020
Cited alongside, same era.
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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Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2021
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Differentially private federated learning with local regularization and sparsification
Anda Cheng, Peisong Wang, Xi Sheryl Zhang, and Jian Cheng · 2022
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Federated class-incremental learning
Jiahua Dong, Lixu Wang, Zhen Fang, Gan Sun, Shichao Xu, Xiao Wang, and Qi Zhu · 2022
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Dytox: Transformers for continual learning with dynamic token expansion
Arthur Douillard, Alexandre Ramé, Guillaume Couairon, and Matthieu Cord · 2022
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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 Papailiopoulos, and Yasaman Khazaeni · 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.
Federated continual learning with weighted inter-client transfer
Jaehong Yoon, Wonyoung Jeong, Giwoong Lee, Eunho Yang, and Sung Ju Hwang · 2020
Cited alongside, same era.
Ss-il: Separated softmax for incremental learning
Hongjoon Ahn, Jihwan Kwak, Subin Lim, Hyeonsu Bang, Hyojun Kim, and Taesup Moon · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Unbiased mean teacher for cross-domain object detection
Jinhong Deng, Wen Li, Yuhua Chen, and Lixin Duan · 2021
Cited alongside, same era.
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Incremental object detection via meta-learning
K. J. Joseph, Jathushan Rajasegaran, Salman Khan, Fahad Shahbaz Khan, and Vineeth N. Balasubramanian · 2022
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Class-incremental learning by knowledge distillation with adaptive feature consolidation
Minsoo Kang, Jaeyoo Park, and Bohyung Han · 2022
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April: Finding the achilles’ heel on privacy for vision transformers
Jiahao Lu, Xi Sheryl Zhang, Tianli Zhao, Xiangyu He, and Jian Cheng · 2022
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Class-incremental learning: Survey and performance evaluation on image classification
Marc Masana, Xialei Liu, Bartłomiej Twardowski, Mikel Menta, Andrew D. Bagdanov, and Joost van de Weijer · 2022
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Rethinking architecture design for tackling data heterogeneity in federated learning
Liangqiong Qu, Yuyin Zhou, Paul Pu Liang, Yingda Xia, Feifei Wang, Ehsan Adeli, Li Fei-Fei, and Daniel Rubin · 2022
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Mimicking the oracle: An initial phase decorrelation approach for class incremental learning
Yujun Shi, Kuangqi Zhou, Jian Liang, Zihang Jiang, Jiashi Feng, Philip H.S. Torr, Song Bai, and Vincent Y. F. Tan · 2022
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Class-incremental learning with strong pre-trained models
Tz-Ying Wu, Gurumurthy Swaminathan, Zhizhong Li, Avinash Ravichandran, Nuno Vasconcelos, Rahul Bhotika, and Stefano Soatto · 2022
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Ideal: Query-efficient data-free learning from black-box models
Jie Zhang, Chen Chen, and Lingjuan Lyu · 2022
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Heterogeneous forgetting compensation for class-incremental learning
Jiahua Dong, Wenqi Liang, Yang Cong, and Gan Sun · 2023
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Dkt: Diverse knowledge transfer transformer for class incremental learning
Xinyuan Gao, Yuhang He, Songlin Dong, Jie Cheng, Xing Wei, and Yihong Gong · 2023
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On the stability-plasticity dilemma of class-incremental learning
Dongwan Kim and Bohyung Han · 2023
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Geometry and uncertainty-aware 3d point cloud class-incremental semantic segmentation
Yuwei Yang, Munawar Hayat, Zhao Jin, Chao Ren, and Yinjie Lei · 2023
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Addressing catastrophic forgetting in federated class-continual learning
Jie Zhang, Chen Chen, Weiming Zhuang, and Lingjuan Lv · 2023
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Delving into the adversarial robustness of federated learning
Jie Zhang, Bo Li, Chen Chen, Lingjuan Lyu, Shuang Wu, Shouhong Ding, and Chao Wu · 2023
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