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Federated learning provides an effective paradigm to jointly optimize a model benefited from rich distributed data while protecting data privacy.
A generalization of Brouwer’s fixed point theorem
Shizuo Kakutani · 1941
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The problem of fair division
Hugo Steinhaus · 1948
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Resource allocation and the public sector
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The core and the lindahl equilibrium of an economy with a public good: An example
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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On approximately fair allocations of indivisible goods
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Uci machine learning repository, 2007
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Learning multiple layers of features from tiny images
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The combinatorial assignment problem: Approximate competitive equilibrium from equal incomes
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Applied logistic regression
David W Hosmer Jr, Stanley Lemeshow, and Rodney X Sturdivant · 2013
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Near fairness in matroids
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Federated learning: Strategies for improving communication efficiency
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Communication-efficient learning of deep networks from decentralized data
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Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
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Justifications of welfare guarantees under normalized utilities
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Optimality and stability in federated learning: A game-theoretic approach
Kate Donahue and Jon M. Kleinberg · 2021
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Fairness-aware agnostic federated learning
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An efficiency-boosting client selection scheme for federated learning with fairness guarantee
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Federated learning on non-iid data silos: An experimental study
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Federating for learning group fair models
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Agnostic federated learning
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Unified group fairness on federated learning
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Fairness and accuracy in horizontal federated learning
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