Journal
PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021)
Volume -, Issue -, Pages 947-956Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3442381.3449888
Keywords
Federated Learning; Incentive Mechanism; Reverse Auction; Reputation
Categories
Funding
- Special Funds for Central Government Guiding Development of Local Science Technology [2020B1515310019]
- National Natural Science Foundation of China [U1911201]
Ask authors/readers for more resources
Current research on federated learning mainly focuses on joint optimization, improving efficiency and effectiveness, as well as protecting privacy, with relatively few studies on incentive mechanisms. This paper proposes an incentive mechanism based on reputation and reverse auction theory, demonstrating its effectiveness by selecting and rewarding participants through bidding for tasks and considering reputation.
Current research on federated learning mainly focuses on joint optimization, improving efficiency and effectiveness, and protecting privacy. However, there are relatively few studies on incentive mechanisms. Most studies fail to consider the fact that if there is no profit, participants have no incentive to provide data and training models, and task requesters cannot identify and select reliable participants with high-quality data. Therefore, this paper proposes a federated learning incentive mechanism based on reputation and reverse auction theory. Participants bid for tasks, and reputation indirectly reflects their reliability and data quality. In this federated learning program, we select and reward participants by combining the reputation and bids of the participants under a limited budget. Theoretical analysis proves that the mechanism satisfies computational efficiency, individual rationality, budget feasibility, and truthfulness. The simulation results show the effectiveness of the mechanism.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available