A Sustainable Incentive Scheme for Federated Learning
| Autor: | Qiang Yang, Dusit Niyato, Han Yu, Mingshu Cong, Tianjian Chen, Xi Weng, Yang Liu, Zelei Liu |
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| Rok vydání: | 2020 |
| Předmět: |
Information privacy
Operations research Computer Networks and Communications business.industry Computer science Stochastic game 02 engineering and technology Commercialization Data modeling Incentive Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Revenue 020201 artificial intelligence & image processing The Internet business Game theory |
| Zdroj: | IEEE Intelligent Systems. 35:58-69 |
| ISSN: | 1941-1294 1541-1672 |
| DOI: | 10.1109/mis.2020.2987774 |
| Popis: | In federated learning (FL), a federation distributedly trains a collective machine learning model by leveraging privacy preserving technologies. However, FL participants need to incur some cost for contributing to the FL models. The training and commercialization of the models will take time. Thus, there will be delays before the federation could pay back the participants. This temporary mismatch between contributions and rewards has not been accounted for by existing payoff-sharing schemes. To address this limitation, we propose the FL incentivizer (FLI). It dynamically divides a given budget in a context-aware manner among data owners in a federation by jointly maximizing the collective utility while minimizing the inequality among the data owners, in terms of the payoff received and the waiting time for receiving payoffs. Comparisons with five state-of-the-art payoff-sharing schemes show that FLI attracts high-quality data owners and achieves the highest expected revenue for a federation. |
| Databáze: | OpenAIRE |
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