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Federated learning provides a promising paradigm for collecting machine learning models from distributed data sources without compromising users' data privacy.
Verification of forecasts expressed in terms of probability
Glenn W. Brier · 1950
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Scoring rules and the evaluation of probability assessors
Robert L. Winkler · 1969
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Elicitation of personal probabilities and expectations
Leonard J. Savage · 1971
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Scoring rules for continuous probability distributions
James E. Matheson and Robert L. Winkler · 1976
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The byzantine generals problem
R. E. Shostak L. Lamport and M. C. Pease · 1982
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The dining cryptographers problem: unconditional sender and recipient untraceability
David Chaum · 1988
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Dining cryptographers revisited
Aris Juels Philippe Golle · 2004
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A bayesian truth serum for subjective data
Dražen Prelec · 2004
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Quantized incremental algorithms for distributed optimization
R. D. Nowak M. G. Rabbat · 2005
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Eliciting informative feedback: The peer-prediction method
N. Miller, P. Resnick, and R. Zeckhauser · 2005
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Eliciting informative feedback: The peer-prediction method
Nolan Miller, Paul Resnick, and Richard Zeckhauser · 2005
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Convexity, classification, and risk bounds
Peter L Bartlett, Michael I Jordan, and Jon D McAuliffe · 2006
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Differential privacy
Cynthia Dwork · 2006
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Scoring rules, generalized entropy and utility maximization
Victor Richmond Jose, Robert F. Nau, and Robert L. Winkler · 2006
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Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E Raftery · 2007
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Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E. Raftery · 2007
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Logarithmic markets coring rules for modular combinatorial information aggregation
Robin Hanson · 2007
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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Differentially private aggregation of distributed time-series with transformation and encryption
Vibhor Rastogi and Suman Nath · 2010
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A collaborative mechanism for crowdsourcing prediction problems
Jacob D Abernethy and Rafael M Frongillo · 2011
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
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A robust bayesian truth serum for small populations
J. Witkowski and D. Parkes · 2012
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Large-scale learning with less ram via randomization
H. Brendan McMahan D. Golovin, D. Sculley and M. Young · 2013
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Crowdsourced judgement elicitation with endogenous proficiency
Anirban Dasgupta and Arpita Ghosh · 2013
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Proactively accountable anonymous messaging in verdict
David Issac Wolinsky Henry Corrigan Gibbs and Bryan Ford · 2013
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Informed truthfulness in multi-task peer prediction
Victor Shnayder, Arpit Agarwal, Rafael Frongillo, and David C Parkes · 2016
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Communication-efficient learning of deep networks from decentralized data
Daniel Ramage Seth Hampson H. Brendan McMahan, Eider Moore and Blaise Aguera y Arcas · 2017
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Machine Learning aided Peer Prediction
Yang Liu and Yiling Chen · 2017
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Federated multi-task learning
Chiang C.-K. Sanjabi m. Smith, V. and A. S Talwalkar · 2017
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Byzantine stochastic gradient descent
Z. Allen-Zhu D. Alistarh and J. Li · 2018
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Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
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A robust bayesian truth serum for non-binary signals
G. Radanovic and B. Faltings · 2013
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Dwelling on the Negative: Incentivizing Effort in Peer Prediction
Jens Witkowski, Yoram Bachrach, Peter Key, and David C. Parkes · 2013
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
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A comprehensive comparison of multiparty secure additions with differential privacy
Slawomir Goryczka and Li Xiong · 2015
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Practical secure aggregation for federated learning on user-held data
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2016
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Qsgd: Randomized quantization for communication-optimal stochastic gradient descent
Ryota Tomioka Dan Alistarh, Jerry Li and Milan Vojnovic · 2016
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Federated learning of n-gram language models
Mingqing Chen, Ananda Theertha Suresh, Rajiv Mathews, Adeline Wong, Cyril Allauzen, Françoise Beaufays, and Michael Riley · 2019
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AdverTorch v0.1: An adversarial robustness toolbox based on pytorch
Gavin Weiguang Ding, Luyu Wang, and Xiaomeng Jin · 2019
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
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An information theoretic framework for designing information elicitation mechanisms that reward truth-telling
Yuqing Kong and Grant Schoenebeck · 2019
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Robust aggregation for federated learning
Krishna Pillutla, Sham M Kakade, and Zaid Harchaoui · 2019
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High-dimensional robust mean estimation in nearly-linear time
I. Diakonikolas Y. Cheng and R. Ge · 2019
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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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Peer loss functions: Learning from noisy labels without knowing noise rates
Yang Liu and Hongyi Guo · 2020
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Surrogate scoring rules
Yang Liu, Juntao Wang, and Yiling Chen · 2020
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Fedcoin: A peer-to-peer payment system for federated learning, 2020
Yuan Liu, Shuai Sun, Zhengpeng Ai, Shuangfeng Zhang, Zelei Liu, and Han Yu · 2020
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A sustainable incentive scheme for federated learning
H. Yu, Z. Liu, Y. Liu, T. Chen, M. Cong, X. Weng, D. Niyato, and Q. Yang · 2020
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A fairness-aware incentive scheme for federated learning
Han Yu, Zelei Liu, Yang Liu, Tianjian Chen, Mingshu Cong, Xi Weng, Dusit Niyato, and Qiang Yang · 2020
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