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Federated learning is typically considered a beneficial technology which allows multiple agents to collaborate with each other, improve the accuracy of their models, and solve problems which are otherwise too data-intensive / expensive to be solved individually.
The existence of equilibrium
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The cancer genome atlas pan-cancer analysis project
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ImageNet Large Scale Visual Recognition Challenge
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Communication-efficient learning of deep networks from decentralized data
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Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation
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Towards federated learning at scale: System design
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Contributors and free-riders in collaborative governance: A computational exploration of social motivation and its effects
Taehyon Choi and Peter J Robertson · 2019
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Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve Gürel, Bo Li, Ce Zhang, Dawn Song, and Costas J Spanos · 2019
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Incentive design for efficient federated learning in mobile networks: A contract theory approach
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Blockchained on-device federated learning
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Agnostic federated learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
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Motivating workers in federated learning: A Stackelberg game perspective
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Data privacy against innovation or against discrimination?: The case of the california consumer privacy act (ccpa)
A principled approach to data valuation for federated learning
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A learning-based incentive mechanism for federated learning
Yufeng Zhan, Peng Li, Zhihao Qu, Deze Zeng, and Song Guo · 2020
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Federated learning used for predicting outcomes in SARS-COV-2 patients
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Free-rider attacks on model aggregation in federated learning
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Advances and open problems in federated learning
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Federated learning for privacy-preserving ai
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A game-theoretic framework for incentive mechanism design in federated learning
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Incentive mechanism design for federated learning with multi-dimensional private information
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An exploratory analysis on users’ contributions in federated learning
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Nonrivalry and the economics of data
Charles I Jones and Christopher Tonetti · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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A survey on federated learning systems: vision, hype and reality for data privacy and protection
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Towards federated learning in uav-enabled internet of vehicles: A multi-dimensional contract-matching approach
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A survey of fairness-aware federated learning
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A contract theory based incentive mechanism for federated learning
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Gradient driven rewards to guarantee fairness in collaborative machine learning
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A survey of incentive mechanism design for federated learning
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