A Survey of Incentive Mechanisms for Federated Learning

Yuzhe Zhou

doi:10.54254/2755-2721/99/20251774

Research Article

Open access

Published on 22 November 2024

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Zhou,Y. (2024). A Survey of Incentive Mechanisms for Federated Learning. Applied and Computational Engineering,99,121-127.

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A Survey of Incentive Mechanisms for Federated Learning

Yuzhe Zhou *^,1,

¹ Queen Mary School Hainan, Beijing University of Posts and Telecommunications, Beijing, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/99/20251774

Abstract

Federated learning is a cutting-edge distributed machine learning technique that lowers the possibility of data leaking from centralized uploads by enabling users to cooperatively train models. It not only protects client privacy but also better utilizes decentralized data resources, demonstrating high learning performance. To employ their models in the training process, however, a significant number of clients must be involved, which inexorably uses up the device's resources—computational power, communication bandwidth, and energy. Therefore, incentivizing more clients to participate in federated learning is crucial. Typical methods can provide numerical or model-based compensation to ensure they contribute their valuable model resources. This paper provides a brief introduction to federated learning and incentive mechanisms, and surveys related research on federated learning incentives. Specifically, this paper categorizes existing federated learning incentive mechanisms into four technical approaches: Shapley values, Stackelberg games, auctions, and contracts. Finally, the paper discusses some future directions for incentivizing clients in federated learning.

Keywords

Federated learning, Incentive Mechanism, Survey.

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References

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Cite this article

Zhou,Y. (2024). A Survey of Incentive Mechanisms for Federated Learning. Applied and Computational Engineering,99,121-127.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Signal Processing and Machine Learning

Conference website: https://2025.confspml.org/

ISBN：978-1-83558-687-7(Print) / 978-1-83558-688-4(Online)

Conference date: 12 January 2025

Editor：Stavros Shiaeles

Series: Applied and Computational Engineering

Volume number: Vol.99

ISSN：2755-2721(Print) / 2755-273X(Online)

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