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Federated learning is a technique that enables a centralized server to learn from distributed clients via communications without accessing the client local data.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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Is learning the n-th thing any easier than learning the first?
Sebastian Thrun · 1995
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Learning task grouping and overlap in multi-task learning
Abhishek Kumar and Hal Daume III · 2012
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Ella: An efficient lifelong learning algorithm
Paul Ruvolo and Eric Eaton · 2013
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al · 2015
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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, et al · 2017
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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Communication-efficient learning of deep networks from decentralized data, 2017
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
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Conditional image synthesis with auxiliary classifier gans
Augustus Odena, Christopher Olah, and Jonathon Shlens · 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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Stabilizing training of generative adversarial networks through regularization
Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, and Thomas Hofmann · 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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Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang · 2017
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Memory aware synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars · 2018
Cited alongside, same era.
Leaf: A benchmark for federated settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
Cited alongside, same era.
Memory replay gans: Learning to generate new categories without forgetting
Chenshen Wu, Luis Herranz, Xialei Liu, Joost van de Weijer, Bogdan Raducanu, et al · 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.
On tiny episodic memories in continual learning
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K Dokania, Philip HS Torr, and Marc’Aurelio Ranzato · 2019
Understanding the role of training regimes in continual learning
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, and Hassan Ghasemzadeh · 2020
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Gdumb: A simple approach that questions our progress in continual learning
Ameya Prabhu, Philip HS Torr, and Puneet K Dokania · 2020
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Fedgan: Federated generative adversarial networks for distributed data
Mohammad Rasouli, Tao Sun, and Ram Rajagopal · 2020
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Improving gan training with probability ratio clipping and sample reweighting
Yue Wu, Pan Zhou, Andrew G Wilson, Eric Xing, and Zhiting Hu · 2020
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Private fl-gan: Differential privacy synthetic data generation based on federated learning
Bangzhou Xin, Wei Yang, Yangyang Geng, Sheng Chen, Shaowei Wang, and Liusheng Huang · 2020
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Cited alongside, same era.
Towards understanding knowledge distillation
Mary Phuong and Christoph Lampert · 2019
Cited alongside, same era.
Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne · 2019
Cited alongside, same era.
Three scenarios for continual learning
Gido M Van de Ven and Andreas S Tolias · 2019
Cited alongside, same era.
Dark experience for general continual learning: a strong, simple baseline
Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, and Simone Calderara · 2020
Cited alongside, same era.
Federated and continual learning for classification tasks in a society of devices
Fernando E Casado, Dylan Lema, Roberto Iglesias, Carlos V Regueiro, and Senén Barro · 2020
Cited alongside, same era.
Fedbe: Making bayesian model ensemble applicable to federated learning
Hong-You Chen and Wei-Lun Chao · 2020
Cited alongside, same era.
Adversarial continual learning
Sayna Ebrahimi, Franziska Meier, Roberto Calandra, Trevor Darrell, and Marcus Rohrbach · 2020
Cited alongside, same era.
Feature normalized knowledge distillation for image classification
Kunran Xu, Lai Rui, Yishi Li, and Lin Gu · 2020
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Federated reconnaissance: Efficient, distributed, class-incremental learning
Sean M Hendryx, Dharma Raj KC, Bradley Walls, and Clayton T Morrison · 2021
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Rebooting acgan: Auxiliary classifier gans with stable training
Minguk Kang, Woohyeon Shim, Minsu Cho, and Jaesik Park · 2021
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A distillation-based approach integrating continual learning and federated learning for pervasive services
Anastasiia Usmanova, François Portet, Philippe Lalanda, and German Vega · 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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Training federated gans with theoretical guarantees: A universal aggregation approach
Yikai Zhang, Hui Qu, Qi Chang, Huidong Liu, Dimitris Metaxas, and Chao Chen · 2021
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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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