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Federated learning involves training statistical models in massive, heterogeneous networks.
On measures of entropy and information
Alfréd Rényi et al · 1961
Earlier work this paper cites.
A quantitative measure of fairness and discrimination
Rajendra K Jain, Dah-Ming W Chiu, and William R Hawe · 1984
Earlier work this paper cites.
Round-robin scheduling for max-min fairness in data networks
Ellen L. Hahne · 1991
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Charging and rate control for elastic traffic
Frank Kelly · 1997
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Uci repository of machine learning databases
Catherine L Blake · 1998
Earlier work this paper cites.
Rate control for communication networks: shadow prices, proportional fairness and stability
Frank P Kelly, Aman K Maulloo, and David KH Tan · 1998
Earlier work this paper cites.
Fair end-to-end window-based congestion control
Jeonghoon Mo and Jean Walrand · 2000
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Thyagarajan Nandagopal, Tae-Eun Kim, Xia Gao, and Vaduvur Bharghavan · 2000
Earlier work this paper cites.
Vehicle classification in distributed sensor networks
Marco F Duarte and Yu Hen Hu · 2004
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Congestion control and fairness for many-to-one routing in sensor networks
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Joint congestion control, routing, and mac for stability and fairness in wireless networks
Atilla Eryilmaz and R Srikant · 2006
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A unified framework for max-min and min-max fairness with applications
Bozidar Radunovic and Jean-Yves Le Boudec · 2007
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Fairness and optimal stochastic control for heterogeneous networks
Michael J Neely, Eytan Modiano, and Chih-Ping Li · 2008
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Twitter sentiment classification using distant supervision
Alec Go, Richa Bhayani, and Lei Huang · 2009
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An axiomatic theory of fairness in network resource allocation
Tian Lan, David Kao, Mung Chiang, and Ashutosh Sabharwal · 2010
Earlier work this paper cites.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Harmonic mean rate fairness for cognitive radio networks with heterogeneous traffic
Mina Dashti, Paeiz Azmi, and Keivan Navaie · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Optimal joint base station assignment and beamforming for heterogeneous networks
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Communication-efficient distributed optimization using an approximate newton-type method
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Fairness in wireless networks: Issues, measures and challenges
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Computational fairness: Preventing machine-learned discrimination
A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, and Hanna Wallach · 2018
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Leaf: A benchmark for federated settings
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Fairness without demographics in repeated loss minimization
Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang · 2018
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Cocoa: A general framework for communication-efficient distributed optimization
Virginia Smith, Simone Forte, Ma Chenxin, Martin Takáč, Michael I Jordan, and Martin Jaggi · 2018
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Jianyu Wang and Gauri Joshi · 2018
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Michael Feldman · 2015
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Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
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Tensorflow: A system for large-scale machine learning
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Equality of opportunity in supervised learning
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Optimized pre-processing for discrimination prevention
Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R Varshney · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Communication-efficient learning of deep networks from decentralized data
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Graph oracle models, lower bounds, and gaps for parallel stochastic optimization
Blake E Woodworth, Jialei Wang, Adam Smith, Brendan McMahan, and Nati Srebro · 2018
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A characterization of guesswork on swiftly tilting curves
Ahmad Beirami, Robert Calderbank, Mark M Christiansen, Ken R Duffy, and Muriel Médard · 2019
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Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, H Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselande · 2019
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Optimization with non-differentiable constraints with applications to fairness, recall, churn, and other goals
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Federated learning: Challenges, methods, and future directions
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Local sgd converges fast and communicates little
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Parallel restarted sgd for non-convex optimization with faster convergence and less communication
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Rényi fair inference
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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
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