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Personalized federated learning considers learning models unique to each client in a heterogeneous network.
Backpropagation applied to handwritten zip code recognition
Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel · 1989
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Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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Multitask learning
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Vehicle classification in distributed sensor networks
Marco F Duarte and Yu Hen Hu · 2004
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A convex formulation for learning task relationships in multi-task learning
Yu Zhang and Dit-Yan Yeung · 2010
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Malsar: Multi-task learning via structural regularization
Jiayu Zhou, Jianhui Chen, and Jieping Ye · 2011
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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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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An overview of multi-task learning in deep neural networks
Sebastian Ruder · 2017
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Federated multi-task learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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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
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Federated meta-learning with fast convergence and efficient communication
Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, and Xiuqiang He · 2018
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Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
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On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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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 with personalization layers
Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary · 2019
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konečnỳ, Stefano Mazzocchi, Brendan McMahan, et al · 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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Improving federated learning personalization via model agnostic meta learning
Yihan Jiang, Jakub Konečnỳ, Keith Rush, and Sreeram Kannan · 2019
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Adaptive gradient-based meta-learning methods
Mikhail Khodak, Maria-Florina F Balcan, and Ameet S Talwalkar · 2019
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Agnostic federated learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
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Federated evaluation of on-device personalization
Kangkang Wang, Rajiv Mathews, Chloé Kiddon, Hubert Eichner, Françoise Beaufays, and Daniel Ramage · 2019
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Flower: A friendly federated learning research framework
Daniel J Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Titouan Parcollet, Pedro PB de Gusmão, and Nicholas D Lane · 2020
Cited alongside, same era.
Fedeval: A benchmark system with a comprehensive evaluation model for federated learning
Di Chai, Leye Wang, Kai Chen, and Qiang Yang · 2020
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Convergence and accuracy trade-offs in federated learning and meta-learning
Zachary Charles and Jakub Konečnỳ · 2021
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On large-cohort training for federated learning
Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, and Virginia Smith · 2021
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Personalized federated learning: A unified framework and universal optimization techniques
Filip Hanzely, Boxin Zhao, and Mladen Kolar · 2021
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Fedgraphnn: A federated learning system and benchmark for graph neural networks
Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Carl Yang, Han Xie, Lichao Sun, Lifang He, Liangwei Yang, Philip S Yu, Yu Rong, et al · 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
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Personalized federated learning with moreau envelopes
Canh T Dinh, Nguyen H Tran, and Tuan Dung Nguyen · 2020
Cited alongside, same era.
Personalized federated learning: A meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 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.
Federated learning of a mixture of global and local models
Filip Hanzely and Peter Richtárik · 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.
Fedml: A research library and benchmark for federated machine learning
Chaoyang He, Songze Li, Jinhyun So, Xiao Zeng, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, et al · 2020
Cited alongside, same era.
Federated visual classification with real-world data distribution
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2020
Cited alongside, same era.
Differentially private model personalization
Prateek Jain, John Rush, Adam Smith, Shuang Song, and Abhradeep Guha Thakurta · 2021
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2021
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Federated hyperparameter tuning: Challenges, baselines, and connections to weight-sharing
Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina F Balcan, Virginia Smith, and Ameet Talwalkar · 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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Adaptive federated optimization
Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and Hugh Brendan McMahan · 2021
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Robust continuous on-device personalization for automatic speech recognition
Khe Chai Sim, Angad Chandorkar, Fan Gao, Mason Chua, Tsendsuren Munkhdalai, and Françoise Beaufays · 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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A field guide to federated optimization
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H Brendan McMahan, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, et al · 2021
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What do we mean by generalization in federated learning?
Honglin Yuan, Warren Richard Morningstar, Lin Ning, and Karan Singhal · 2021
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A survey on multi-task learning
Yu Zhang and Qiang Yang · 2021
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pfl-bench: A comprehensive benchmark for personalized federated learning
Daoyuan Chen, Dawei Gao, Weirui Kuang, Yaliang Li, and Bolin Ding · 2022
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Patching open-vocabulary models by interpolating weights
Gabriel Ilharco, Mitchell Wortsman, Samir Yitzhak Gadre, Shuran Song, Hannaneh Hajishirzi, Simon Kornblith, Ali Farhadi, and Ludwig Schmidt · 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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Fedscale: Benchmarking model and system performance of federated learning at scale
Fan Lai, Yinwei Dai, Sanjay S Singapuram, Jiachen Liu, Xiangfeng Zhu, Harsha V Madhyastha, and Mosharaf Chowdhury · 2022
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Personalized federated learning through local memorization
Othmane Marfoq, Giovanni Neglia, Richard Vidal, and Laetitia Kameni · 2022
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An empirical study of personalized federated learning
Koji Matsuda, Yuya Sasaki, Chuan Xiao, and Makoto Onizuka · 2022
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Towards personalized federated learning
Alysa Ziying Tan, Han Yu, Lizhen Cui, and Qiang Yang · 2022
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Diurnal or nocturnal? federated learning of multi-branch networks from periodically shifting distributions
Chen Zhu, Zheng Xu, Mingqing Chen, Jakub Konečnỳ, Andrew Hard, and Tom Goldstein · 2022
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When and why are pre-trained word embeddings useful for neural machine translation?
Ye Qi, Devendra Sachan, Matthieu Felix, Sarguna Padmanabhan, and Graham Neubig · 2084
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