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Federated learning is a new distributed machine learning framework, where a bunch of heterogeneous clients collaboratively train a model without sharing training data.
Gradient-Based Learning Applied to Document Recognition
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Dataset Shift in Machine Learning
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A Unifying View on Dataset Shift in Classification
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Scaling Distributed Machine Learning with the Parameter Server. In Proceedings of OSDI . 583–598
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Federated Learning for Mobile Keyboard Prediction
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Sparsified SGD with Memory. In Proceedings of NeurIPS . 4447–4458
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Applied Federated Learning: Improving Google Keyboard Query Suggestions
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Towards Federated Learning at Scale: System Design. In Proceedings of MLSys
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Semi-Cyclic Stochastic Gradient Descent. In Proceedings of ICML . 1764–1773
Hubert Eichner, Tomer Koren, Brendan McMahan, Nathan Srebro, and Kunal Talwar. 2019 · 2019
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The Non-IID Data Quagmire of Decentralized Machine Learning
Kevin Hsieh, Amar Phanishayee, Onur Mutlu, and Phillip B. Gibbons. 2019 · 2019
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Advances and Open Problems in Federated Learning
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Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport. In Proceedings of AISTATS . 3118–3127
Adarsh Subbaswamy, Peter Schulam, and Suchi Saria. 2019 · 2019
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Yae Jee Cho, Jianyu Wang, and Gauri Joshi. 2020 · 2020
Closest in time.
Distributed Machine Learning through Heterogeneous Edge Systems. In Proceedings of AAAI . 7179–7186
Hanpeng Hu, Dan Wang, and Chuan Wu. 2020 · 2020
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Scaffold: Stochastic Controlled Averaging for Federated Learning. In Proceedings of ICML
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Closest in time.
Towards Flexible Device Participation in Federated Learning for Non-IID Data
Yichen Ruan, Xiaoxi Zhang, Shu-Che Liang, and Carlee Joe-Wong. 2020 · 2020
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Cited alongside, same era.
First Analysis of Local GD on Heterogeneous Data
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik. 2019 · 2019
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Federated Learning: Challenges, Methods, and Future Directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2019a · 2019
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Agnostic Federated Learning. In Proceedings of ICML . 4615–4625
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh. 2019 · 2019
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Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty under Dataset Shift. In Proceedings of NeurIPS . 13969–13980
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Local SGD Converges Fast and Communicates Little. In Proceedings of ICLR
Sebastian U. Stich. 2019 · 2019
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Sebastian U. Stich and Sai Praneeth Karimireddy. 2019 · 2019
Cited alongside, same era.
RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets. In Proceedings of AAAI . 1544–1551
Liping Li, Wei Xu, Tianyi Chen, Georgios B. Giannakis, and Qing Ling. 2019b
Cited in the paper.
Federated Optimization in Heterogeneous Networks. In Proceedings of MLSys
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020b
Cited in the paper.
Closest in time.
Network-Aware Optimization of Distributed Learning for Fog Computing. In Proceedings of INFOCOM . 2509–2518
Yuwei Tu, Yichen Ruan, Satyavrat Wagle, Christopher G. Brinton, and Carlee Joe-Wong. 2020 · 2020
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Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization. In Proceedings of NeurIPS
Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H. Vincent Poor. 2020 · 2020
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Federated Variance-Reduced Stochastic Gradient Descent With Robustness to Byzantine Attacks
Zhaoxian Wu, Qing Ling, Tianyi Chen, and Georgios B. Giannakis. 2020 · 2020
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Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability
Yikai Yan, Chaoyue Niu, Yucheng Ding, Zhenzhe Zheng, Fan Wu, Guihai Chen, Shaojie Tang, and Zhihua Wu. 2020 · 2020
Closest in time.
Communication-Efficient Algorithms for Statistical Optimization. In Proceedings of NeurIPS . 1502–1510
Yuchen Zhang, Martin J Wainwright, and John C Duchi. 2012 · 2020
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