2022
DOI: 10.48550/arxiv.2203.03885
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Incentivizing Data Contribution in Cross-Silo Federated Learning

Abstract: In cross-silo federated learning, clients (e.g., organizations) collectively train a global model using their local data. However, due to business competitions and privacy concerns, the clients tend to free-ride (i.e., not contribute enough data points) during training. To address this issue, we propose a framework where the profit/benefit obtained from the global model can be properly allocated to clients to incentivize data contribution. More specifically, we study the game-theoretical interactions among the… Show more

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Cited by 1 publication
(2 citation statements)
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References 23 publications
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“…The aggregation server has to obtain the required budget for the rewards. Huang et al model their incentive framework as a game theory [84]. The idea is to monetize the global model and allocate the profits to each participant according to their contributions.…”
Section: ) Free-riding Attacksmentioning
confidence: 99%
See 1 more Smart Citation
“…The aggregation server has to obtain the required budget for the rewards. Huang et al model their incentive framework as a game theory [84]. The idea is to monetize the global model and allocate the profits to each participant according to their contributions.…”
Section: ) Free-riding Attacksmentioning
confidence: 99%
“…Huang et al [84] Incentive framework based on game theory, which monetizes the global model and allocates profits to each participants.…”
Section: Richardson Et Al [83]mentioning
confidence: 99%