2021
DOI: 10.48550/arxiv.2109.09046
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Improving Fairness for Data Valuation in Horizontal Federated Learning

Zhenan Fan,
Huang Fang,
Zirui Zhou
et al.

Abstract: Federated learning is an emerging decentralized machine learning scheme that allows multiple data owners to work collaboratively while ensuring data privacy. The success of federated learning depends largely on the participation of data owners. To sustain and encourage data owners' participation, it is crucial to fairly evaluate the quality of the data provided by the data owners and reward them correspondingly. Federated Shapley value, recently proposed by Wang et al. [Federated Learning, 2020], is a measure … Show more

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Cited by 2 publications
(6 citation statements)
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“…Federated Shapley value does not need model retraining and preserves some but not all of the favorable qualities of the traditional Shapley value. Fan et al [9] further improved the fairness of this approach.…”
Section: Related Workmentioning
confidence: 99%
See 2 more Smart Citations
“…Federated Shapley value does not need model retraining and preserves some but not all of the favorable qualities of the traditional Shapley value. Fan et al [9] further improved the fairness of this approach.…”
Section: Related Workmentioning
confidence: 99%
“…Although the Shapley value has many desirable characteristics, its evaluation in the FL context requires repeatedly training and evaluating a machine learning model on all possible subsets of clients. The corresponding communication and computational costs are exponential, and thus prohibitive in practice [29,34,9].…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…To achieve this, the central server needs to effectively and fairly evaluate the clients' contribution. Existing studies mainly focus on three types of contribution evaluation/profit allocation mechanisms: linear proportional [11], leave-one-out [10], [12], and Shapley-value-based methods [13], [20], [21]. However, these papers focus on deriving efficient and fair algorithms for contribution valuation/profit allocation, rather than analyzing their impact on the clients' free rider behaviors.…”
Section: Related Workmentioning
confidence: 99%
“…A reasonable profit allocation mechanism should reward those who contribute more data in training to incentivize data contribution and eliminate free riders. In this paper, we consider three widely adopted profit allocation mechanisms, i.e., linearly proportional (LP) where each client's contribution is measured proportionally to its data size [11]; leave-one-out (LOO) where the contribution is calculated by the difference of the global model accuracy between with and without the client's participation [12]; and Shapley value (SV) where the contribution is measured by the average marginal influence on the global model accuracy [13].…”
Section: Introductionmentioning
confidence: 99%