A Sustainable Incentive Scheme for Federated Learning

By Han Yu, Zelei Liu, Yang Liu, Tianjian Chen, Mingshu Cong, Xi Weng, Dusist Niyato, Qiang Yang. 2020

119 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Science Applications, Federated & Distributed Learning

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Citations in the corpus

Federated Learning: Opportunities and Challenges (2021, background): “Client-selection and incentive designs[37] are some notable techniques needed to facilitate this type of FL.”
A Survey on Federated Learning for Resource-Constrained IoT Devices (2021, unknown): “With the motivation of addressing issues related to costs and mismatch between client’s contributions and receiving incentives, Yu et al. [116], [122] proposed a payoff-sharing scheme named FL Incentivizer (FLI).”
Decentralized Federated Learning: A Survey and Perspective (2023, background): “…values indicating the relative strength of the respective FL approach in that domain. metrics, which are the focal points of current state-of-the-art (SOTA) research and worthy of further investigation [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48].”