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Distributed (or Federated) learning enables users to train machine learning models on their very own devices, while they share only the gradients of their models usually in a differentially private way (utility loss).
Learning from imbalanced data
H. He and E. A. Garcia · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
Earlier work this paper cites.
Multiparty differential privacy via aggregation of locally trained classifiers
M. A. Pathak, S. Rane, and B. Raj · 2010
Earlier work this paper cites.
Federated learning: Strategies for improving communication efficiency
J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon · 2016
Earlier work this paper cites.
Benchmarking paillier encryption
M. Dahl · 2017
Earlier work this paper cites.
LEAF: A benchmark for federated settings
S. Caldas, P. Wu, T. Li, J. Konečný, H. B. McMahan, V. Smith, and A. Talwalkar · 2018
Earlier work this paper cites.
Paillier homomorphic encryption
M. Dahl, M. Cornejo, M. Poumeyrol, O. Shlomovits, R. Zeyde, and G. Benattar · 2018
Earlier work this paper cites.
Inference attacks against collaborative learning
L. Melis, C. Song, E. De Cristofaro, and V. Shmatikov · 2018
Cited alongside, same era.
Privacy-preserving deep learning via additively homomorphic encryption
L. T. Phong, Y. Aono, T. Hayashi, L. Wang, and S. Moriai · 2018
Cited alongside, same era.
Privacy preserving distributed deep learning and its application in credit card fraud detection
Y. Wang, S. Adams, P. Beling, S. Greenspan, S. Rajagopalan, M. Velez-Rojas, S. Mankovski, S. Boker, and D. Brown · 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.
Biscotti: A ledger for private and secure peer-to-peer machine learning, 2019
M. Shayan, C. Fung, C. J. M. Yoon, and I. Beschastnikh · 2019
Cited alongside, same era.
Ramp - rust arithmetic in multiple precision
J. Miller · 2020
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Degree-quant: Quantization-aware training for graph neural networks
S. A. Tailor, J. Fernandez-Marques, and N. D. Lane · 2020
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Privacy-preserving distributed machine learning via local randomization and admm perturbation
X. Wang, H. Ishii, L. Du, P. Cheng, and J. Chen · 2020
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How to use AirDrop on your iPhone, iPad, or iPod touch
Apple Inc · 2021
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Gfl: A decentralized federated learning framework based on blockchain, 2021
Y. Hu, Y. Zhou, J. Xiao, and C. Wu · 2021
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https://www.kaggle.com/c/avito-context-ad-clicks/overview
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Proximity and cross device communication
Google Developers · 2020
Cited alongside, same era.
Towards byzantine-resilient learning in decentralized systems
S. Guo, T. Zhang, X. Xie, L. Ma, T. Xiang, and Y. Liu · 2020
Cited alongside, same era.
On decentralizing federated learning
A. Agrawal, D. D. Kulkarni, and S. B. Nair
Cited in the paper.
Personalized and private peer-to-peer machine learning
A. Bellet, R. Guerraoui, M. Taziki, and M. Tommasi
Cited in the paper.
Machine learning with adversaries: Byzantine tolerant gradient descent
P. Blanchard, E. M. El Mhamdi, R. Guerraoui, and J. Stainer
Cited in the paper.
Practical secure aggregation for privacy-preserving machine learning
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth
Cited in the paper.
Robust p2p personalized learning
K. Boubouh, A. Boussetta, Y. Benkaouz, and R. Guerraoui
Cited in the paper.
Avito context ad clicks · 2022
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Monsoon solutions inc
Monsoon · 2022
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