## 2 Related Works  
There are two main techniques for task offloading in SEC: centralized and distributed approaches. In centralized ones, offloading schemes of satellites are typically generated by a central server [14]. Some works [16; 17] employed exact methods to obtain optimal offloading assignments. For example, [PERSON] et al. [16] divided the computation offloading problem into the ground and space segments and proposed an energy-efficient offloading method. Similar to [16], [PERSON] et al. [17] decomposed the optimization problem into four subproblems, and quadratic transform-based fractional programming and the interior point method were used. Since the task offloading problem is NP-hard [29], exact methods can be effective only for small instances. Hence, heuristic and meta-heuristic approaches were adopted [14; 15; 18]. Specifically, [PERSON] et al. [14] established a greedy based task allocation algorithm. Considering the efficiency of the meta-heuristic algorithm in solving the NP-hard problem, [PERSON] et al. [15] and [PERSON] et al. [18] introduced particle swarm optimization-based methods for task offloading in SEC. Additionally, reinforcement learning methods [19; 20; 22; 23; 30; 31] are also employed to quickly obtain high-quality offloading assignments. Specifically, [PERSON] et al. [19] first developed a deep Q-learning approach for offloading problems. [PERSON] et al. [20] embedded the long short-term memory model into a learning-based offloading strategy, addressing the dynamics of energy harvesting performance. Considering the data dependence among tasks, [PERSON] et al. [21] proposed a deep imitation learning-driven offloading and caching algorithm. To achieve cooperative scheduling with both tasks and resources, [PERSON] et al. [22] proposed a mixed algorithm combining deep reinforcement learning with convex optimization. [PERSON] et al. [23] proposed a deep deterministic policy gradient-based algorithm outputting both discrete and continuous variables, achieving simultaneous decisions on offloading locations and allocating resources. To address security concerns in SEC, [PERSON] et al. [30] developed a deep deterministic policy gradient-based security-aware offloading algorithm. Subsequently, [PERSON] et al. [31] introduced a federated reinforcement learning-based offloading method to enhance privacy protection. Centralized approaches are usually easier to implement and run faster, but may have the following drawbacks: (1) the central server requires consistent communication with each satellite, leading to a heavy communication burden. For ground-based central servers especially [17; 22; 32], it also occupies satellite-terrestrial network resources; (2) a high computational demand is caused by generating all offloading assignments and monitoring any state changes of satellites during task processing; and (3) there is a resulting single point of failure. Considering the non-centrality of SEC, distributed approaches [24; 25; 26; 27; 28] are adopted. Specifically, [PERSON] et al. [24] adopted a contract net protocol (CNP)-based method to resolve the autonomous mission planning problem. Works [25; 26] employed the diffusion algorithm-based approach. [PERSON] et al. [25] introduced a transmission capacity and computing capacity-aware diffusion algorithm to obtain task offloading assignments. Then, based on [25], [PERSON] et al. [26] proposed a directed diffusion-enhanced task scheduling algorithm (DETS), which embedded a computing gradient that considered both the computing and communication resources. Other works [27; 28] used the distributed convex optimization-based approach. For example, [PERSON] et al. [27] employed a binary-variable relaxation method to convert the original nonconvex problem into a linear programming problem, then proposed an alternating-direction method of multipliers (ADMM)-based distributed computation offloading scheme. Finally, a binary-variable recovery algorithm was adopted to obtain the required discrete offloading assignment. Similarly, [PERSON] et al.