Academic Journal of Computing & Information Science, 2023, 6(6); doi: 10.25236/AJCIS.2023.060605.
Bingze Li
School of Management Engineering and Business, Hebei University of Engineering, Handan, China
Federated learning has been widely paid attention as a new type of distributed machine learning that can protect data privacy and ensure data security. Asynchronous federation learning, a variant of traditional federation learning, can effectively improve model training efficiency. The introduction of incentive mechanism can help asynchronous federation learning to improve model training utility effectively. A federated learning incentive mechanism is constructed using the Stackelberg game, which optimizes the central server and data owner utilities, respectively, of the Stackelberg game. Based on this, we derive the equilibrium solution of the whole game, and finally analyze the feasibility of the model by arithmetic examples to obtain the optimal incentive effect.
Federated learning, Stackelberg game, Robust optimization, Data quality, Nash equilibrium
Bingze Li. Design of asynchronous federated learning incentive mechanism. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 6: 31-36. https://doi.org/10.25236/AJCIS.2023.060605.
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