Poster: Computation-efficient Decentralized Federated Learning in LEO Satellite Network

Decentralized satellite federated learning (DSFL) enables LEO satellites to collaboratively train models without relying on terrestrial infrastructures. In DSFL, concurrent model updates for multiple services increase the amount of computation on the satellite, which can lead to longer total training time. This paper proposes a computation-efficient DSFL framework to construct the overlay network for services to minimize the total training time. Simulation results demonstrate that the proposed scheme outperforms the baseline algorithms in terms of total training time.