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Federated Learning (FL) is an emerging paradigm that allows a model to be trained across a number of participants without sharing data.
Connectionist models of recognition memory: constraints imposed by learning and forgetting functions
Roger Ratcliff · 1990
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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Visual domain adaptation: A survey of recent advances
Vishal M Patel, Raghuraman Gopalan, Ruonan Li, and Rama Chellappa · 2014
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Jake Zhao, and Yann LeCun · 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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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <0.5mb model size
Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, and Kurt Keutzer · 2016
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Federated optimization: Distributed machine learning for on-device intelligence
Jakub Konečnỳ, H Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
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A survey of transfer learning
Karl Weiss, Taghi M Khoshgoftaar, and DingDing Wang · 2016
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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
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 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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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clement Hongler · 2018
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Jianyu Wang and Gauri Joshi · 2018
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Rethinking imagenet pre-training
Kaiming He, Ross Girshick, and Piotr Dollar · 2019
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2019
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Do better imagenet models transfer better?
Simon Kornblith, Jonathon Shlens, and Quoc V Le · 2019
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A comprehensive survey on transfer learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He · 2020
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Federated learning based on dynamic regularization
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N Whatmough, and Venkatesh Saligrama · 2021
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Fine-tuning with differential privacy necessitates an additional hyperparameter search
Yannis Cattan, Christopher A Choquette-Choo, Nicolas Papernot, and Abhradeep Thakurta · 2022
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Probing representation forgetting in supervised and unsupervised continual learning
MohammadReza Davari, Nader Asadi, Sudhir Mudur, Rahaf Aljundi, and Eugene Belilovsky · 2022
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Head2toe: Utilizing intermediate representations for better transfer learning
Utku Evci, Vincent Dumoulin, Hugo Larochelle, and Michael C Mozer · 2022
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2019
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Local sgd converges fast and communicates little
Sebastian U Stich · 2019
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Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning
Hao Yu, Sen Yang, and Shenghuo Zhu · 2019
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A survey on transfer learning in natural language processing
Zaid Alyafeai, Maged Saeed AlShaibani, and Irfan Ahmad · 2020
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Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2020
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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
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A simple baseline that questions the use of pretrained-models in continual learning
Paul Janson, Wenxuan Zhang, Rahaf Aljundi, and Mohamed Elhoseiny · 2022
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Fine-tuning can distort pretrained features and underperform out-of-distribution
Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, and Percy Liang · 2022
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Scaling & shifting your features: A new baseline for efficient model tuning
Dongze Lian, Zhou Daquan, Jiashi Feng, and Xinchao Wang · 2022
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Revisiting learnable affines for batch norm in few-shot transfer learning
Moslem Yazdanpanah, Aamer Abdul Rahman, Muawiz Chaudhary, Christian Desrosiers, Mohammad Havaei, Eugene Belilovsky, and Samira Ebrahimi Kahou · 2022
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On the importance and applicability of pre-training for federated learning
Hong-You Chen, Cheng-Hao Tu, Ziwei Li, Han-Wei Shen, and Wei-Lun Chao · 2023
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Re-weighted softmax cross-entropy to control forgetting in federated learning
Gwen Legate, Lucas Caccia, and Eugene Belilovsky · 2023
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Where to begin? on the impact of pre-training and initialization in federated learning
John Nguyen, Jianyu Wang, Kshitiz Malik, Maziar Sanjabi, and Michael Rabbat · 2023
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Fit: Parameter efficient few-shot transfer learning for personalized and federated image classification
Aliaksandra Shysheya, John F Bronskill, Massimiliano Patacchiola, Sebastian Nowozin, and Richard E Turner · 2023
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