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We study a family of algorithms, which we refer to as local update methods, that generalize many federated learning and meta-learning algorithms.
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Communication-efficient learning of deep networks from decentralized data
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How to backdoor federated learning
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Federated learning for ultra-reliable low-latency V2V communications
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Jianyu Wang and Gauri Joshi · 2018
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Robust and communication-efficient federated learning from non-IID data
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Local SGD converges fast and communicates little
Sebastian U. Stich · 2019
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Sebastian U Stich and Sai Praneeth Karimireddy · 2019
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Can you really backdoor federated learning?
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Meta-learning
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Federated learning powered by NVIDIA Clara, December 2019
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Towards federated learning at scale: System design
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Federated learning of n-gram language models
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On the convergence theory of gradient-based model-agnostic meta-learning algorithms
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Robust federated learning in a heterogeneous environment
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The non-IID data quagmire of decentralized machine learning
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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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Local AdaAlter: Communication-efficient stochastic gradient descent with adaptive learning rates
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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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Hao Yu, Sen Yang, and Shenghuo Zhu · 2019
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Generative models for effective ML on private, decentralized datasets
Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, and Blaise Aguera y Arcas · 2020
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Is local SGD better than minibatch SGD?
Blake Woodworth, Kumar Kshitij Patel, Sebastian U Stich, Zhen Dai, Brian Bullins, H Brendan McMahan, Ohad Shamir, and Nathan Srebro · 2020
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