## 3 EDIJP Framework
### EDIJP Overview  
We divide the LEO satellites involved in distributed inference into two categories: requester and workers. The requester is the satellite that generates the inference request, it will give the input data, and the final inference result will also be returned to it. Workers are all the edge satellites that are considered for distributed inference; these are responsible for completing the tasks the requester assigns, including executing inference and data transmission. It is worth noting that an edge satellite can be both a requester and a worker. In once-distributed inference, there is only one requester, but there can be more than one worker.  
Distributed task partitioning and deployment algorithms should be written into the resources of each satellite and integrated into a Joint Partition Module (JPM). We will provide detailed explanations of the JPM module in Section 4. As shown in Figure 5, we describe the EDIJP framework as the preparation phase and the execution phase. The partitioning and deployment decisions will be formulated in the preparation phase through the JPM. The JPM is integrated into every worker, and the worker who generates inference requests is also called a requester, which will generate deployment decisions using JPM based on the situations of computing resources and bandwidth resources. In the preparation phase, model partitioning acts on the original model, generating several sub-models that are executed sequentially. Data partitioning acts on the output data of each sub-model, allowing the inference task of the sub-models to be completed in parallel by multiple edge satellites.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & EDIJP & CoEdge & EdgeFlow \\ \hline Data partition & & & \\ Model partition & & & \\ Synchronous waiting & & & \\ Frequent communication & & & \\ Overlapping computing & & & \\ \hline \hline \end{tabular} a involved; b not involved.  
\end{table}
Table 2: Comparision of different frameworks.  
As shown in Figure 5, there are three workers in the system, names \(w_{1}\), \(w_{2}\), and \(w_{3}\). The CNN model contains five network layers, which are two convolution layers (CLs), one pooling layer (PL), and two fully connection layers (FCLs). After the model partition, the original model is divided into three sub-models. The sub-model \(sm_{3}\) that contains the two FCLs is the classification part in the original model, which will be directly deployed to a worker without any other partition operation. The other two sub-models are further processed by data partition, arranging the inference task to multiple workers. After the preparation phase, the JMP generates data transmission, execution, and sub-model information, which helps to arrange tasks for all the workers. For the convenience of description, we have separately described the results of model partitioning and data partitioning, but this does not mean that there is a clear order between model partitioning and data partitioning during JPM execution.  
After the requester generates a partitioning and deployment plan, the inference tasks are distributed to the workers. At this point, the preparation phase ends and the execution phase begins. Each worker receives and executes the assigned task, and one worker may be assigned multiple tasks. After receiving all the required input data, the worker performs sub-model inference and then transmits the generated intermediate data to the output destination workers; we will describe the data transmission rule in Section 3.2.