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Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to learn collaboratively without sharing their private data.
Transfer learning for image classification with sparse prototype representations
Ariadna Quattoni, Michael Collins, and Trevor Darrell · 2008
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Learning with whom to share in multi-task feature learning
Zhuoliang Kang, Kristen Grauman, and Fei Sha · 2011
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Geodesic flow kernel for unsupervised domain adaptation
Boqing Gong, Yuan Shi, Fei Sha, and Kristen Grauman · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton · 2012
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Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Towards universal paraphrastic sentence embeddings
John Wieting, Mohit Bansal, Kevin Gimpel, and Karen Livescu · 2015
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Aggregating local deep features for image retrieval
Artem Babenko and Victor Lempitsky · 2015
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Microsoft coco captions: Data collection and evaluation server
Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollár, and C Lawrence Zitnick · 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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The quick, draw!-AI experiment
Jonas Jongejan, Henry Rowley, Takashi Kawashima, Jongmin Kim, and Nick Fox-Gieg · 2016
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Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, et al · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2018
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Synthetic to real adaptation with generative correlation alignment networks
Xingchao Peng and Kate Saenko · 2018
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Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama · 2018
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Improving federated learning personalization via model agnostic meta learning
Yihan Jiang, Jakub Konečnỳ, Keith Rush, and Sreeram Kannan · 2019
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Unsupervised embedding learning via invariant and spreading instance feature
Mang Ye, Xu Zhang, Pong C Yuen, and Shih-Fu Chang · 2019
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Learning to propagate for graph meta-learning
Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, and Chengqi Zhang · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
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Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
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Fair resource allocation in federated learning
Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith · 2019
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Fedbn: Federated learning on non-IID features via local batch normalization
Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, and Qi Dou · 2020
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Personalized federated learning: A meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2020
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Personalized federated learning with moreau envelopes
Canh T Dinh, Nguyen Tran, and Tuan Dung Nguyen · 2020
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Federated learning for open banking
Guodong Long, Yue Tan, Jing Jiang, and Chengqi Zhang · 2020
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Decentralized knowledge acquisition for mobile internet applications
Jing Jiang, Shaoxiong Ji, and Guodong Long · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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A universal representation transformer layer for few-shot image classification
Lu Liu, William L Hamilton, Guodong Long, Jing Jiang, and Hugo Larochelle · 2020
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Meta-dataset: A dataset of datasets for learning to learn from few examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, et al · 2020
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Learning to generate novel domains for domain generalization
Kaiyang Zhou, Yongxin Yang, Timothy Hospedales, and Tao Xiang · 2020
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Personalized federated learning with moreau envelopes
Canh T Dinh, Nguyen H Tran, and Tuan Dung Nguyen · 2020
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Anomaly detection on attributed networks via contrastive self-supervised learning
Yixin Liu, Zhao Li, Shirui Pan, Chen Gong, Chuan Zhou, and George Karypis · 2021
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Towards graph self-supervised learning with contrastive adjusted zooming
Yizhen Zheng, Ming Jin, Shirui Pan, Yuan-Fang Li, Hao Peng, Ming Li, and Zhao Li · 2021
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Model-contrastive federated learning
Qinbin Li, Bingsheng He, and Dawn Song · 2021
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Fedproc: Prototypical contrastive federated learning on non-IID data
Xutong Mu, Yulong Shen, Ke Cheng, Xueli Geng, Jiaxuan Fu, Tao Zhang, and Zhiwei Zhang · 2021
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Prototype guided federated learning of visual feature representations
Umberto Michieli and Mete Ozay · 2021
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Lower bounds and optimal algorithms for personalized federated learning
Filip Hanzely, Slavomír Hanzely, Samuel Horváth, and Peter Richtárik · 2020
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Personalized federated learning with first order model optimization
Michael Zhang, Karan Sapra, Sanja Fidler, Serena Yeung, and Jose M Alvarez · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2020
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Federated unsupervised representation learning
Fengda Zhang, Kun Kuang, Zhaoyang You, Tao Shen, Jun Xiao, Yin Zhang, Chao Wu, Yueting Zhuang, and Xiaolin Li · 2020
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Confusable learning for large-class few-shot classification
Bingcong Li, Bo Han, Zhuowei Wang, Jing Jiang, and Guodong Long · 2020
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Learning task-agnostic embedding of multiple black-box experts for multi-task model fusion
Nghia Hoang, Thanh Lam, Bryan Kian Hsiang Low, and Patrick Jaillet · 2020
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Pre-trained models: Past, present and future
Xu Han, Zhengyan Zhang, Ning Ding, Yuxian Gu, Xiao Liu, Yuqi Huo, Jiezhong Qiu, Liang Zhang, Wentao Han, Minlie Huang, et al · 2021
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Parameterized knowledge transfer for personalized federated learning
Jie Zhang, Song Guo, Xiaosong Ma, Haozhao Wang, Wenchao Xu, and Feijie Wu · 2021
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A practical data-free approach to one-shot federated learning with heterogeneity
Jie Zhang, Chen Chen, Bo Li, Lingjuan Lyu, Shuang Wu, Jianghe Xu, Shouhong Ding, and Chao Wu · 2021
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Temperature as uncertainty in contrastive learning
Oliver Zhang, Mike Wu, Jasmine Bayrooti, and Noah Goodman · 2021
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Pre-trained image processing transformer
Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao · 2021
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Logme: Practical assessment of pre-trained models for transfer learning
Kaichao You, Yong Liu, Jianmin Wang, and Mingsheng Long · 2021
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Federated asymptotics: a model to compare federated learning algorithms
Gary Cheng, Karan Chadha, and John Duchi · 2021
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CalFAT: Calibrated federated adversarial training with label skewness
Chen Chen, Yuchen Liu, Xingjun Ma, and Lingjuan Lyu · 2022
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Personalized federated learning with structure
Fengwen Chen, Guodong Longr, Zonghan Wu, Tianyi Zhou, and Jing Jiang · 2022
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Federated learning for privacy-preserving open innovation future on digital health
Guodong Long, Tao Shen, Yue Tan, Leah Gerrard, Allison Clarke, and Jing Jiang · 2022
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Quantum collective learning and many-to-many matching game in the metaverse for connected and autonomous vehicles
Yuzheng Ren, Renchao Xie, F. Richard Yu, Tao Huang, and Yunjie Liu · 2022
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FedProto: Federated prototype learning over heterogeneous devices
Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, and Chengqi Zhang · 2022
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Towards unsupervised deep graph structure learning
Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng, and Shirui Pan · 2022
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Rethinking and scaling up graph contrastive learning: An extremely efficient approach with group discrimination
Yizhen Zheng, Shirui Pan, Vincent Cs Lee, Yu Zheng, and Philip S Yu · 2022
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Green intelligence networking for connected and autonomous vehicles in smart cities
Yuzheng Ren, Renchao Xie, Fei Richard Yu, Tao Huang, and Yunjie Liu · 2022
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Multi-center federated learning: clients clustering for better personalization
Guodong Long, Ming Xie, Tao Shen, Tianyi Zhou, Xianzhi Wang, and Jing Jiang · 2022
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On the convergence of clustered federated learning
Jie Ma, Guodong Long, Tianyi Zhou, Jing Jiang, and Chengqi Zhang · 2022
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Practical attribute reconstruction attack against federated learning
Chen Chen, Lingjuan Lyu, Han Yu, and Gang Chen · 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, Li Fei-Fei, Ehsan Adeli, and Daniel Rubin · 2022
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SemiNLL: A framework of noisy-label learning by semi-supervised learning
Zhuowei Wang, Jing Jiang, Bo Han, Lei Feng, Bo An, Gang Niu, and Guodong Long · 2022
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FedNoiL: A simple two-level sampling method for federated learning with noisy labels
Zhuowei Wang, Tianyi Zhou, Guodong Long, Bo Han, and Jing Jiang · 2022
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Fine-tuning global model via data-free knowledge distillation for non-IID federated learning
Lin Zhang, Li Shen, Liang Ding, Dacheng Tao, and Ling-Yu Duan · 2022
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