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With the increasing development of Low-Earth-Orbit (LEO) Satelite Communications (SatComs), it is foreseen that they will play an important role in broadening the horizon of Federated Learning (FL). Specifically, SatComs can amplify FL by providing consistent global transmission, bridging terrestrial network gaps, and ensuring robust, reliable connectivity in remote or challenging terrains. In this chapter, we consider a SatComs-based FL framework, where satellites in the low-earth orbit collaborate to serve as global servers, able to collect and aggregate FL model parameters transmitted from the mobile devices on the ground continuously.

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Incentive Mechanism Design in Satellite-Based Federated Learning Using Mean Field Evolutionary Approach

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  • 发表日期：

    2025年

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  • Yuhan Kang,
  • Hao Gao,
  • Zhu Han

摘要

With the increasing development of Low-Earth-Orbit (LEO) Satelite Communications (SatComs), it is foreseen that they will play an important role in broadening the horizon of Federated Learning (FL). Specifically, SatComs can amplify FL by providing consistent global transmission, bridging terrestrial network gaps, and ensuring robust, reliable connectivity in remote or challenging terrains. In this chapter, we consider a SatComs-based FL framework, where satellites in the low-earth orbit collaborate to serve as global servers, able to collect and aggregate FL model parameters transmitted from the mobile devices on the ground continuously.