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In federated learning, a strong global model is collaboratively learned by aggregating clients' locally trained models.
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Federated optimization: Distributed machine learning for on-device intelligence
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Federated learning: Strategies for improving communication efficiency
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Improved regularization of convolutional neural networks with cutout
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Overcoming catastrophic forgetting in neural networks
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Efficient lifelong learning with a-gem
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Cinic-10 is not imagenet or cifar-10
Luke N Darlow, Elliot J Crowley, Antreas Antoniou, and Amos J Storkey · 2018
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Local sgd converges fast and communicates little
Sebastian U Stich · 2018
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Federated learning with non-iid data
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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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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
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Fedmd: Heterogenous federated learning via model distillation
Daliang Li and Junpu Wang · 2019
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On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2019
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Continual lifelong learning with neural networks: A review
Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
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Ensemble distillation for robust model fusion in federated learning
Tao Lin, Lingjing Kong, Sebastian U Stich, and Martin Jaggi · 2020
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Class-incremental learning: survey and performance evaluation on image classification
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Federated learning with matched averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni · 2020
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Tackling the objective inconsistency problem in heterogeneous federated optimization
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German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Overcoming forgetting in federated learning on non-iid data
Neta Shoham, Tomer Avidor, Aviv Keren, Nadav Israel, Daniel Benditkis, Liron Mor-Yosef, and Itai Zeitak · 2019
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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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Bayesian nonparametric federated learning of neural networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, and Yasaman Khazaeni · 2019
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Federated learning: A survey on enabling technologies, protocols, and applications
Mohammed Aledhari, Rehma Razzak, Reza M Parizi, and Fahad Saeed · 2020
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Cpr: Classifier-projection regularization for continual learning
Sungmin Cha, Hsiang Hsu, Taebaek Hwang, Flavio P Calmon, and Taesup Moon · 2020
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Using hindsight to anchor past knowledge in continual learning
Arslan Chaudhry, Albert Gordo, Puneet Kumar Dokania, Philip Torr, and David Lopez-Paz · 2020
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Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H Vincent Poor · 2020
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Is local sgd better than minibatch sgd?
Blake Woodworth, Kumar Kshitij Patel, Sebastian Stich, Zhen Dai, Brian Bullins, Brendan Mcmahan, Ohad Shamir, and Nathan Srebro · 2020
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Distilled one-shot federated learning
Yanlin Zhou, George Pu, Xiyao Ma, Xiaolin Li, and Dapeng Wu · 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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Federated learning on non-iid data silos: An experimental study
Qinbin Li, Yiqun Diao, Quan Chen, and Bingsheng He · 2021
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Model-contrastive federated learning
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No fear of heterogeneity: Classifier calibration for federated learning with non-iid data
Mi Luo, Fei Chen, Dapeng Hu, Yifan Zhang, Jian Liang, and Jiashi Feng · 2021
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Federated continual learning with weighted inter-client transfer
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Fedmix: Approximation of mixup under mean augmented federated learning
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Fed2: Feature-aligned federated learning
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Data-free knowledge distillation for heterogeneous federated learning
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Federated class-incremental learning
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Local learning matters: Rethinking data heterogeneity in federated learning
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Acceleration of federated learning with alleviated forgetting in local training
Chencheng Xu, Zhiwei Hong, Minlie Huang, and Tao 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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