The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
A Survey of Incentive Mechanism Design for Federated Learning
Abstract
Federated learning is promising in enabling large-scale machine learning by massive clients without exposing their raw data. It can not only enable the clients to preserve the privacy information, but also achieve high learning performance. Existing works of federated learning mainly focus on improving learning performance in terms of model accuracy and learning task completion time. However, in practice, clients are reluctant to participate in the learning process without receiving compensation. Therefore, how to effectively motivate the clients to actively and reliably participate in federated learning is paramount. As compared to the current incentive mechanism design in other fields, such as crowdsourcing, cloud computing, smart grid, etc., the incentive mechanism for federated learning is more challenging. First, it is hard to evaluate the training data value of each client. Second, it is difficult to model the learning performance of different federated learning algorithms. In this paper, we survey the incentive mechanism design for federated learning. In particular, we present a taxonomy of existing incentive mechanisms for federated learning, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, some future directions of how to incentivize clients in federated learning have been discussed.
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Cites
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Reliable Federated Learning for Mobile Networks
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Self-citationA Learning-Based Incentive Mechanism for Federated Learning
Zhan, Y., Li, P., Qu, Z., Zeng, D., & Guo, S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2967772 · Cited by 602
Self-citationMachine Learning Models that Remember Too Much
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Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism
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Cited by
Federated learning for smart healthcare: A survey
Nguyen, D. C., Pham, Q.-V., Pathirana, P. N., Ding, M., Seneviratne, A., Lin, Z., Dobre, O., & Hwang, W.-J. (2023) · ACM Computing Surveys · doi:10.1145/3501296 · Cited by 827
Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey
Ali, M., Naeem, F., Tariq, M., & Kaddoum, G. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3181823 · Cited by 366
Blockchain-enabled Federated Learning: A Survey
Qu, Y., Uddin, M. P., Gan, C., Xiang, Y., Gao, L., & Yearwood, J. (2023) · ACM Computing Surveys · doi:10.1145/3524104 · Cited by 286
Fu, Y., Li, C., Yu, F. R., Luan, T. H., & Zhao, P. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3297996 · Cited by 96
Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges, and Future Directions
Hafi, H., Brik, B., Frangoudis, P. A., Ksentini, A., & Bagaa, M. (2024) · IEEE Access · doi:10.1109/access.2024.3351600 · Cited by 90
Metadata
- Authors
- Zhan, Y., Zhang, J., Hong, Z., Wu, L., Li, P., & Guo, S.
- Venue
- IEEE Transactions on Emerging Topics in Computing
- Published
- 2021
- Pages
- 1-1
- ISSN
- 2168-6750 (ISSN-L)
- 2376-4562
- OpenAlex
- W3135472452
- Cited by
- 284
- References
- 40
- FWCI
- 18.88
- Type
- journal article
- Language
- en
- Open access
- closed
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