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An oft-cited open problem of federated learning is the existence of data heterogeneity at the clients.
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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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Learning multiple layers of features from tiny images
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Curriculum learning of multiple tasks
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Communication-efficient learning of deep networks from decentralized data
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An empirical exploration of curriculum learning for neural machine translation
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Curriculumnet: Weakly supervised learning from large-scale web images
Sheng Guo, Weilin Huang, Haozhi Zhang, Chenfan Zhuang, Dengke Dong, Matthew R. Scott, and Dinglong Huang · 2018
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Self-paced prioritized curriculum learning with coverage penalty in deep reinforcement learning
Zhipeng Ren, Daoyi Dong, Huaxiong Li, and Chunlin Chen · 2018
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On the convergence properties of a k-step averaging stochastic gradient descent algorithm for nonconvex optimization
Fan Zhou and Guojing Cong · 2018
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An empirical study of example forgetting during deep neural network learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J. Gordon · 2018
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Curriculum learning for domain adaptation in neural machine translation
Xuan Zhang, Pamela Shapiro, Gaurav Kumar, Paul McNamee, Marine Carpuat, and Kevin Duh · 2019
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Self paced deep learning for weakly supervised object detection
Enver Sangineto, Moin Nabi, Dubravko Culibrk, and Nicu Sebe · 2019
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Simple and effective curriculum pointer-generator networks for reading comprehension over long narratives
Yi Tay, Shuohang Wang, Anh Tuan Luu, Jie Fu, Minh C. Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, and Aston Zhang · 2019
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Multi-modal curriculum learning over graphs
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On the power of curriculum learning in training deep networks
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On the convergence of local descent methods in federated learning
Farzin Haddadpour and Mehrdad Mahdavi · 2019
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Optimal client sampling for federated learning
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Yae Jee Cho, Jianyu Wang, and Gauri Joshi · 2020
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Adaptive personalized federated learning
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On the convergence of sgd with biased gradients
Ahmad Ajalloeian and Sebastian U Stich · 2020
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Tighter theory for local sgd on identical and heterogeneous data
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik · 2020
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Local sgd converges fast and communicates little
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Breaking the curse of space explosion: Towards efficient NAS with curriculum search
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Exploring the memorization-generalization continuum in deep learning
Ziheng Jiang, Chiyuan Zhang, Kunal Talwar, and Michael C. Mozer · 2020
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Curriculum learning by dynamic instance hardness
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Federated optimization in heterogeneous networks
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Federated optimization in heterogeneous networks
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ST3D: self-training for unsupervised domain adaptation on 3d object detection
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When do curricula work?
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Memory-aware curriculum federated learning for breast cancer classification
Amelia Jiménez-Sánchez, Mickael Tardy, Miguel Ángel González Ballester, Diana Mateus, and Gemma Piella · 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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Federated learning on non-iid data: A survey
Hangyu Zhu, Jinjin Xu, Shiqing Liu, and Yaochu Jin · 2021
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Budgeted online selection of candidate iot clients to participate in federated learning
Ihab Mohammed, Shadha Tabatabai, Ala I. Al-Fuqaha, Faissal El Bouanani, Junaid Qadir, Basheer Qolomany, and Mohsen Guizani · 2021
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Saeed Vahidian, Mahdi Morafah, Weijia Wang, Vyacheslav Kungurtsev, Chen Chen, Mubarak Shah, and Bill Lin · 2022
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A multi-agent reinforcement learning approach for efficient client selection in federated learning
Sai Qian Zhang, Jieyu Lin, and Qi Zhang · 2022
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