Improving Fairness for Data Valuation in Horizontal Federated ...

We propose a new measure called completed federated Shapley value to improve the fairness of federated Shapley value. The design depends on ...

To make fair evaluation of data owners practical in federated learning, some variations inspired by Shapley value were proposed. For example, Wang et al. [6] ...

Improving Fairness for Data Valuation in Horizontal Federated ...

Federated learning is an emerging decentralized machine learning scheme that allows multiple data owners to work collaboratively while ensuring data privacy ...

[PDF] Improving Fairness for Data Valuation in Horizontal Federated ...

This work proposes a new measure called completed federated Shapley value, a measure for data value under the framework of federated learning that satisfies ...

Improving Fairness for Data Valuation in Horizontal Federated ...

Request PDF | On May 1, 2022, Zhenan Fan and others published Improving Fairness for Data Valuation in Horizontal Federated Learning | Find, read and cite ...

Improving Fairness for Data Valuation in Federated Learning

However, there are still factors of potential unfairness in the design of federated Shapley value because two data owners with the same local ...

Improving Fairness for Data Valuation in Federated Learning

Improving Fairness for Data Valuation in Federated Learning · Zhenan Fan, Huang Fang, +4 authors. Yong Zhang · Published in arXiv.org 2021 · Computer Science, ...

Improving fairness for data valuation in federated learning

Improving fairness for data valuation in federated learning. Publication , Journal Article. Fan, Z; Fang, H; Zhou, Z; Pei, J; Friedlander, MP; Liu, C; Zhang ...

Publications | Zhenan Fan (范喆楠)

[Federated Learning, 2020], is a measure for data value under the framework of federated learning that satisfies many desired properties for data valuation.

Poster: Verifiable Data Valuation with Strong Fairness in Horizontal ...

Data valuation for each data provider becomes a critical issue to guarantee the fairness of federated learning by estimating the dataset quality ...

FAIR AND EFFICIENT CONTRIBUTION VALUATION FOR ...

Improving fairness for data valuation in horizontal federated learning. In 2022 IEEE 38th. International Conference on Data Engineering (ICDE), pp. 2440–2453 ...

Fairness and accuracy in horizontal federated learning - ScienceDirect

This process assists clients in aggregating at the server with a more fair weighting. Our results show that the proposed FedFa algorithm outperforms the ...

daviddao/awesome-data-valuation - GitHub

This paper focuses on fairness in data valuation within federated learning. The authors propose a new measure called completed federated Shapley value to ...

Efficient and Fair Data Valuation for Horizontal Federated Learning

In this work, we adapt Shapley value, a widely used data valuation metric to valuating data providers in federated learning. Prior data valuation schemes for ...

Enhancing Fairness in Federated Learning: A Contribution‐Based ...

This mechanism encourages clients to make greater contributions for improved global models. Experimental results confirm the effectiveness of ...

A Study on Fairness in a Dynamically Growing Federated Learning ...

weighted fair data sampler algorithm to enhance fairness in training data. ... Improving fairness for data valuation in horizontal federated learning. In ...

Data Valuation in Federated Learning with Jian Pei - YouTube

Abstract: To enable practical federated learning, we not only have to improve the efficiency but also address the incentive and fairness ...

IMPROVING FAIRNESS VIA FEDERATED LEARNING - OpenReview

The pink horizontal plane visualizes the value of δ (the lowest DP disparity that UFL can ... 1https://anonymous.4open.science/r/Improving-Fairness-via-Data- ...

Understanding Practical Value of Data Assets in Federated Learning

In this work, we present a novel framework that combines Federated Learning and Blockchain by Shapley value (FLBS) to achieve a good trade-off between privacy ...

Fairness in Trustworthy Federated Learning: A Survey

Improving fairness for data valuation in horizontal federated learning[C]//2022 IEEE 38th International Conference on Data Engineering (ICDE). Kuala Lumpur ...