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Personalized Federated Learning (pFL), which utilizes and deploys distinct local models, has gained increasing attention in recent years due to its success in handling the statistical heterogeneity of FL clients.
Automating the construction of internet portals with machine learning
Andrew Kachites McCallum, Kamal Nigam, Jason Rennie, and Kristie Seymore · 2000
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Link-based classification
Lise Getoor · 2005
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Differential privacy: A survey of results
Cynthia Dwork · 2008
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Fast unfolding of communities in large networks
Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Matrix factorization techniques for recommender systems
Yehuda Koren, Robert M. Bell, and Chris Volinsky · 2009
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Torchvision the machine-vision package of torch
Sébastien Marcel and Yann Rodriguez · 2010
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Query-driven active surveying for collective classification
Galileo Namata, Ben London, Lise Getoor, Bert Huang, and U Edu · 2012
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan · 2015
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
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Federated multi-task learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet Talwalkar · 2017
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EMNIST: extending MNIST to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik · 2017
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Hyperband: Bandit-based configuration evaluation for hyperparameter optimization
Lisha Li, Kevin G. Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar · 2017
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Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
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Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
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Applied federated learning: Improving google keyboard query suggestions
Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, and Françoise Beaufays · 2018
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Leaf: A benchmark for federated settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečný, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman · 2018
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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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SCAFFOLD: stochastic controlled averaging for on-device federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, and Ananda Theertha Suresh · 2019
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Towards taming the resource and data heterogeneity in federated learning
Zheng Chai, Hannan Fayyaz, Zeshan Fayyaz, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Heiko Ludwig, and Yue Cheng · 2019
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Variational federated multi-task learning, 2019
Luca Corinzia and Joachim M. Buhmann · 2019
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Adaptive gradient-based meta-learning methods
Mikhail Khodak, Maria-Florina Balcan, and Ameet Talwalkar · 2019
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Improving federated learning personalization via model agnostic meta learning
Yihan Jiang, Jakub Konečný, Keith Rush, and Sreeram Kannan · 2019
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Towards federated learning at scale: System design
Kallista A. Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konečný, Stefano Mazzocchi, Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander · 2019
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Well-read students learn better: On the importance of pre-training compact models
Iulia Turc, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konečný, Stefano Mazzocchi, Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander · 2019
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Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R Bowman · 2019
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Fedvision: An online visual object detection platform powered by federated learning
Yang Liu, Anbu Huang, Yun Luo, He Huang, Youzhi Liu, Yuanyuan Chen, Lican Feng, Tianjian Chen, Han Yu, and Qiang Yang · 2020
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Empirical studies of institutional federated learning for natural language processing
Xinghua Zhu, Jianzong Wang, Zhenhou Hong, and Jing Xiao · 2020
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Feded: Federated learning via ensemble distillation for medical relation extraction
Dianbo Sui, Yubo Chen, Jun Zhao, Yantao Jia, Yuantao Xie, and Weijian Sun · 2020
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Fedfast: Going beyond average for faster training of federated recommender systems
Khalil Muhammad, Qinqin Wang, Diarmuid O’Reilly-Morgan, Elias Z. Tragos, Barry Smyth, Neil Hurley, James Geraci, and Aonghus Lawlor · 2020
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Survey of personalization techniques for federated learning
Viraj Kulkarni, Milind Kulkarni, and Aniruddha Pant · 2020
Cited alongside, same era.
An efficient framework for clustered federated learning
Avishek Ghosh, Jichan Chung, Dong Yin, and Kannan Ramchandran · 2020
Cited alongside, same era.
Personalized federated learning with moreau envelopes
Canh T. Dinh, Nguyen H. Tran, and Tuan Dung Nguyen · 2020
Cited alongside, same era.
Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman E. Ozdaglar · 2020
Cited alongside, same era.
SCAFFOLD: stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, and Ananda Theertha Suresh · 2020
Cited alongside, same era.
Fed2: Feature-aligned federated learning
Fuxun Yu, Weishan Zhang, Zhuwei Qin, Zirui Xu, Di Wang, Chenchen Liu, Zhi Tian, and Xiang Chen · 2021
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A survey on security and privacy of federated learning
Viraaji Mothukuri, Reza M. Parizi, Seyedamin Pouriyeh, Yan Huang, Ali Dehghantanha, and Gautam Srivastava · 2021
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Towards personalized federated learning
Alysa Ziying Tan, Han Yu, Lizhen Cui, and Qiang Yang · 2021
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Exploiting shared representations for personalized federated learning
Liam Collins, Hamed Hassani, Aryan Mokhtari, and Sanjay Shakkottai · 2021
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Ditto: Fair and robust federated learning through personalization
Tian Li, Shengyuan Hu, Ahmad Beirami, and Virginia Smith · 2021
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Fedopt: Towards communication efficiency and privacy preservation in federated learning
Muhammad Asad, Ahmed Moustafa, and Takayuki Ito · 2020
Cited alongside, same era.
Tackling the objective inconsistency problem in heterogeneous federated optimization
Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H. Vincent Poor · 2020
Cited alongside, same era.
Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
Cited alongside, same era.
Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2020
Cited alongside, same era.
Federated learning with hierarchical clustering of local updates to improve training on non-iid data
Christopher Briggs, Zhong Fan, and Peter Andras · 2020
Cited alongside, same era.
Tifl: A tier-based federated learning system
Zheng Chai, Ahsan Ali, Syed Zawad, Stacey Truex, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Heiko Ludwig, Feng Yan, and Yue Cheng · 2020
Cited alongside, same era.
Lower bounds and optimal algorithms for personalized federated learning
Filip Hanzely, Slavomír Hanzely, Samuel Horváth, and Peter Richtárik · 2020
Cited alongside, same era.
Zhuangdi Zhu, Junyuan Hong, and Jiayu Zhou · 2021
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Federated multi-task learning under a mixture of distributions
Othmane Marfoq, Giovanni Neglia, Aurélien Bellet, Laetitia Kameni, and Richard Vidal · 2021
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Personalized federated learning using hypernetworks
Aviv Shamsian, Aviv Navon, Ethan Fetaya, and Gal Chechik · 2021
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Personalized federated learning with first order model optimization
Michael Zhang, Karan Sapra, Sanja Fidler, Serena Yeung, and Jose M. Alvarez · 2021
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Heterofl: Computation and communication efficient federated learning for heterogeneous clients
Enmao Diao, Jie Ding, and Vahid Tarokh · 2021
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Oort: Efficient federated learning via guided participant selection
Fan Lai, Xiangfeng Zhu, Harsha V. Madhyastha, and Mosharaf Chowdhury · 2021
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Datasets: A community library for natural language processing
Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, Abhishek Thakur, Patrick von Platen, Suraj Patil, Julien Chaumond, Mariama Drame, Julien Plu, Lewis Tunstall, Joe Davison, Mario Šaško, Gunjan Chhablani, Bhavitvya Malik, Simon Brandeis, Teven Le Scao, Victor Sanh, Canwen Xu, Nicolas Patry, Angelina McMillan-Major, Philipp Schmid, Sylvain Gugger, Clément Delangue, Théo Matussière, Lysandre Debut, Stas Bekman, Pierric Cistac, Thibault Goehringer, Victor Mustar, François Lagunas, Alexander Rush, and Thomas Wolf · 2021
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FL-NTK: A neural tangent kernel-based framework for federated learning analysis
Baihe Huang, Xiaoxiao Li, Zhao Song, and Xin Yang · 2021
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Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints
Felix Sattler, Klaus-Robert Müller, and Wojciech Samek · 2021
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Personalized cross-silo federated learning on non-iid data
Yutao Huang, Lingyang Chu, Zirui Zhou, Lanjun Wang, Jiangchuan Liu, Jian Pei, and Yong Zhang · 2021
Later among the works it cites.
Federated multi-task learning under a mixture of distributions
Othmane Marfoq, Giovanni Neglia, Aurélien Bellet, Laetitia Kameni, and Richard Vidal · 2021
Later among the works it cites.
Personalized federated learning with first order model optimization
Michael Zhang, Karan Sapra, Sanja Fidler, Serena Yeung, and Jose M. Alvarez · 2021
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Personalized federated learning with gaussian processes
Idan Achituve, Aviv Shamsian, Aviv Navon, Gal Chechik, and Ethan Fetaya · 2021
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Quped: Quantized personalization via distillation with applications to federated learning
Kaan Ozkara, Navjot Singh, Deepesh Data, and Suhas N. Diggavi · 2021
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Federated reconstruction: Partially local federated learning
Karan Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, John Rush, and Sushant Prakash · 2021
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Debiasing model updates for improving personalized federated training
Durmus Alp Emre Acar, Yue Zhao, Ruizhao Zhu, Ramon Matas Navarro, Matthew Mattina, Paul N. Whatmough, and Venkatesh Saligrama · 2021
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Fedsteg: A federated transfer learning framework for secure image steganalysis
Hongwei Yang, Hui He, Weizhe Zhang, and Xiaochun Cao · 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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Fednlp: A research platform for federated learning in natural language processing
Bill Yuchen Lin, Chaoyang He, Zihang Zeng, Hulin Wang, Yufen Huang, Mahdi Soltanolkotabi, Xiang Ren, and Salman Avestimehr · 2021
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Adaptive federated optimization
Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and Hugh Brendan McMahan · 2021
Later among the works it cites.
Personalized federated learning using hypernetworks
Aviv Shamsian, Aviv Navon, Ethan Fetaya, and Gal Chechik · 2021
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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 · 2021
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Federated matrix factorization with privacy guarantee
Zitao Li, Bolin Ding, Ce Zhang, Ninghui Li, and Jingren Zhou · 2021
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Fedbabu: Toward enhanced representation for federated image classification
Jaehoon Oh, Sangmook Kim, and Se-Young Yun · 2022
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Fedscale: Benchmarking model and system performance of federated learning at scale
Fan Lai, Yinwei Dai, Sanjay Sri Vallabh Singapuram, Jiachen Liu, Xiangfeng Zhu, Harsha V. Madhyastha, and Mosharaf Chowdhury · 2022
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What do we mean by generalization in federated learning?
Honglin Yuan, Warren Richard Morningstar, Lin Ning, and Karan Singhal · 2022
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Federatedscope-gnn: Towards a unified, comprehensive and efficient package for federated graph learning
Zhen Wang, Weirui Kuang, Yuexiang Xie, Liuyi Yao, Yaliang Li, Bolin Ding, and Jingren Zhou · 2022
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Federatedscope: A comprehensive and flexible federated learning platform via message passing
Yuexiang Xie, Zhen Wang, Daoyuan Chen, Dawei Gao, Liuyi Yao, Weirui Kuang, Yaliang Li, Bolin Ding, and Jingren Zhou · 2022
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Federated learning with buffered asynchronous aggregation
John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Mike Rabbat, Mani Malek, and Dzmitry Huba · 2022
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