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Federated learning (FL) aims to train machine learning models in the decentralized system consisting of an enormous amount of smart edge devices.
Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
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Federated optimization: Distributed optimization beyond the datacenter
J. Konečnỳ, B. McMahan, and D. Ramage · 2015
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Federated optimization: Distributed machine learning for on-device intelligence
J. Konečnỳ, H. B. McMahan, D. Ramage, and P. Richtárik · 2016
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Federated learning: Strategies for improving communication efficiency
J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon · 2016
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Emnist: an extension of mnist to handwritten letters
G. Cohen, S. Afshar, J. Tapson, and A. van Schaik · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 2017
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Accurate, large minibatch sgd: Training imagenet in 1 hour
P. Goyal, P. Dollár, R. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, and K. He · 2017
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Meta-sgd: Learning to learn quickly for few-shot learning
Z. Li, F. Zhou, F. Chen, and H. Li · 2017
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Communication-efficient learning of deep networks from decentralized data
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas · 2017
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cpsgd: Communication-efficient and differentially-private distributed sgd
N. Agarwal, A. T. Suresh, F. X. X. Yu, S. Kumar, and B. McMahan · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
J. Buolamwini and T. Gebru · 2018
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Expanding the reach of federated learning by reducing client resource requirements
S. Caldas, J. Konečny, H. B. McMahan, and A. Talwalkar · 2018
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Leaf: A benchmark for federated settings
S. Caldas, P. Wu, T. Li, J. Konečnỳ, H. B. McMahan, V. Smith, and A. Talwalkar · 2018
Cited alongside, same era.
Why is my classifier discriminatory?
I. Chen, F. D. Johansson, and D. Sontag · 2018
Cited alongside, same era.
E. Jeong, S. Oh, H. Kim, J. Park, M. Bennis, and S.-L. Kim · 2018
Cited alongside, same era.
Federated optimization in heterogeneous networks
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith · 2018
Cited alongside, same era.
Secure federated transfer learning
Y. Liu, T. Chen, and Q. Yang · 2018
Secureboost: A lossless federated learning framework
K. Cheng, T. Fan, Y. Jin, Y. Liu, T. Chen, and Q. Yang · 2019
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Robust federated training via collaborative machine teaching using trusted instances
Y. Han and X. Zhang · 2019
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Learning private neural language modeling with attentive aggregation
S. Ji, S. Pan, G. Long, X. Li, J. Jiang, and Z. Huang · 2019
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Improving federated learning personalization via model agnostic meta learning
Y. Jiang, J. Konečnỳ, K. Rush, and S. Kannan · 2019
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On the convergence of fedavg on non-iid data
X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang · 2019
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Cited alongside, same era.
Federated optimization for heterogeneous networks
A. K. Sahu, T. Li, M. Sanjabi, M. Zaheer, A. Talwalkar, and V. Smith · 2018
Cited alongside, same era.
Two-stream federated learning: Reduce the communication costs
X. Yao, C. Huang, and L. Sun · 2018
Cited alongside, same era.
Federated learning with non-iid data
Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra · 2018
Cited alongside, same era.
Ai can be sexist and racist-it’s time to make it fair
J. Zou and L. Schiebinger · 2018
Cited alongside, same era.
How to train your maml
A. Antoniou, H. Edwards, and A. Storkey · 2019
Cited alongside, same era.
Analyzing federated learning through an adversarial lens
A. N. Bhagoji, S. Chakraborty, P. Mittal, and S. Calo · 2019
Cited alongside, same era.
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Agnostic federated learning
M. Mohri, G. Sivek, and A. T. Suresh · 2019
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Social data: Biases, methodological pitfalls, and ethical boundaries
A. Olteanu, C. Castillo, F. Diaz, and E. Kiciman · 2019
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Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
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Robust and communication-efficient federated learning from non-iid data
F. Sattler, S. Wiedemann, K.-R. Müller, and W. Samek · 2019
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Federated learning with additional mechanisms on clients to reduce communication costs
X. Yao, T. Huang, C. Wu, R.-X. Zhang, and L. Sun · 2019
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Personalized federated learning: A meta-learning approach
A. Fallah, A. Mokhtari, and A. Ozdaglar · 2020
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