A Survey of Incentive Mechanism Design for Federated Learning

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 improv...

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Vydané v:IEEE transactions on emerging topics in computing Ročník 10; číslo 2; s. 1035 - 1044
Hlavní autori: Zhan, Yufeng, Zhang, Jie, Hong, Zicong, Wu, Leijie, Li, Peng, Guo, Song
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Clients
Cloud computing
Cognitive tasks
Collaborative work
Completion time
Computational modeling
Data models
Federated learning
incentive mechanism
Machine learning
Model accuracy
Servers
Smart grid
survey
Task analysis
Taxonomy
Training
Training evaluation
ISSN:2168-6750, 2168-6750
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Shrnutí: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 article, 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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2168-6750
2168-6750
DOI:10.1109/TETC.2021.3063517