QuoTa: An Online Quality-Aware Incentive Mechanism for Fast Federated Learning

In addition to federated optimization, more current studies focus on incentive mechanism design problems for federated learning (FL), stimulating data owners to share their resources securely. Most existing works only considered data quantity but neglected other key factors like data quality and tra...

Full description

Saved in:
Bibliographic Details
Published in:Applied sciences 2024-01, Vol.14 (2), p.833
Main Authors: Cai, Hui, Bian, Chao, Sheng, Biyun, Zhou, Jian, Li, Juan, He, Xin
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Auctions
Communication
Design
Energy consumption
federated learning
Guarantees
incentive mechanism
Information management
multi-dimensional reverse auction
Poisoning
Privacy
Reputation management
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In addition to federated optimization, more current studies focus on incentive mechanism design problems for federated learning (FL), stimulating data owners to share their resources securely. Most existing works only considered data quantity but neglected other key factors like data quality and training time prediction. In combination with all the above factors, we proposed an online quality-aware incentive mechanism based on multi-dimensional reverse auction, QuoTa, for achieving fast FL. In particular, it first designs model quality detection to eliminate some malicious or dispensable devices based on their historical behaviors and marginal contributions. Due to the possible fluctuations of CPU frequency in realistic model training, it next predicts model training time based on the upper confidence bound algorithm. By combining the two modules, QuoTa incentivizes data owners with high data quality, high computing capability, and low cost to participate in the FL process. By rigorous theoretical proof and extensive experiments, we prove that QuoTa satisfies all desired economic properties and achieves higher model accuracy and less convergence time than the state-of-the-art work.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14020833