## 2 Related Work
### Satellite-Based Edge Computing  
In recent years, more and more researchers have begun to recognize the importance of satellite-based edge computing. [PERSON] et al. introduce a novel satellite-terrestrial network with double edge computing to reap the benefits of providing computing services to remote areas [16]. A strategy is designed to solve the problem of efficiently scheduling the edge servers distributed in the satellite-terrestrial networks to provide more powerful edge-computing services. For the purpose of allocating satellite edge-computing resources efficiently in the strategy, a double-edge-computation offloading algorithm is proposed to optimize energy consumption and reduce latency by assigning tasks to edge servers with minimal cost. [PERSON] et al. propose a game-theoretic approach to the optimization of computation offloading strategy in satellite edge computing [17]. The system model for computation offloading in satellite edge computing is established, considering the intermittent terrestrial-satellite communication caused by satellites orbiting. They conduct a computation offloading game framework and compute the response time and energy consumption of a task based on the queuing theory as metrics of optimizing performance. [PERSON] et al. believe that orbital edge-computing (OEC) technology that deploys edge-computing servers on LEO satellite constellations can meet the growing demand for the real time reliability of various applications [18]. Based on the above motivations, they propose an OEC task allocation (OECTA) algorithm based on the greedy strategy in LEO satellite networks for the Walker Delta satellite constellation, which fully utilizes satellite computing resources to provide services to ground users. They also analyze the performance of their proposed algorithm in terms of computational cost. [PERSON] et al. observe that, different from terrestrial edge computing, the computing capacity in LEO satellites is usually unstable due to the limited and consistently changing energy supply of fast-orbiting LEO satellites [19]. They propose a dynamic offloading strategy to minimize the overall delay of tasks from terrestrial users in a satellite edge-computing system, subject to the energy and computing capacity constraints of the LEO satellite. Based on Lyapunov optimization theory, they convert a long-term stochastic problem with a time-varying energy constraint into multiple deterministic one-slot problems parameterized by the current system state, in which task-offloading decisions, computing resource allocation and transmit power control are jointly optimized. [PERSON] and his colleagues investigated a framework for optimizing image distribution and parameter compression in LEO edge computing [20]. Their approach increases the number of supported images by the system while reducing energy consumption. Their research primarily focuses on energy efficiency. [PERSON] and his team studied task-offloading algorithms based on deep reinforcement learning, achieving better offloading cost effectiveness [21]. Their approach is suitable for satellite-terrestrial networks with fast fading channels, and their computational tasks lean more towards task offloading rather than multi-satellite collaborative inference. As we can see from these related works, present research in the field of LEO edge computing predominantly focuses on task offloading, emphasizing the transfer of computational tasks from users to satellites to enhance computational efficiency in the LEO satellite network. These works encompass a wide range of topics, including deep reinforcement learning, distributed task scheduling and model optimization, with the aim of addressing resource constraints and energy limitations. However, research on collaborative inference among satellites remains relatively scarce, leaving a critical gap in the current body of knowledge.