# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 4 Answering the RQs
### RQ2--Techniques and Strategies
#### 4.2.5 Scheduling  
A different set of frameworks for addressing EI problems is focused on where to offload the intelligence tasks of the application (edge or cloud). While the mechanism of model partitioning addresses the division of the model between those locations, in the scheduling paradigm, intelligence is implemented at both layers. The application needs to choose at which layer the tasks will more efficiently be offloaded. For example, [PERSON] et al. [90] propose a two-tier offloading method, in which the optimized decision is made according to the trade-off between low latency and energy saving. [PERSON] et al. [91] split human fall detection between edge devices and servers in the cloud, with front-end IoT devices performing lightweight computation for fall detection, while data are also transmitted to the back-end cloud servers for more intensive computation. The framework proposed by [PERSON] et al. [67] offloads sound manipulation algorithms to both layers, keeping more complicated processing in the cloud.  
A framework encompassing storage and offloading techniques for edge computing applications is presented by [PERSON] et al. [36]. The authors introduce a dynamic offload method according to the capabilities of the edge devices. Based on values of latency, bandwidth, processing and memory, the execution time of the application is predicted to partition it.  
The scheduling technique in EI can be employed in many research fields. [PERSON] et al. [41] use scheduling in a satellite network. The satellites are distributed into layers from the edge to the cloud, where the computing capability gradually increases. Computing with low complexity can be performed on the satellite IoT fog node. However, if local computing and storage resources are insufficient, the target detection data may be offloaded to a nearby satellite edge or cloud node. Computing with high complexity and requirements is suitable for completion in the satellite IoT cloud node.  
Authors [PERSON], [PERSON], and [PERSON] [92] train the NN with large-scale data in the cloud, then train and customize the pre-trained model with small-scale data in the edge and offload tasks to appropriate servers. For mobile devices that do not find an appropriate edge or remote server, the tasks can be accomplished locally at their CPU. For computing tasks with predicted delay to the remote cloud lower than their delay requirement, the computing tasks tend to be routed to the remote cloud, since it is more powerful and can provide the highest accuracy. Therefore, the authors seek to achieve accuracy maximization offloading with latency constraints (AMLC).  
[PERSON] and [PERSON] [93] focus on how to coordinate the edge and cloud to train EI models, with a view to minimizing resource usage. Training data are buffered in a queue before being scheduled to the edge or cloud for processing. They quantify the degree of privacy preservation with the ratio of the total amount that are processed in the cloud to the total amount that arrive in the long term, where a smaller ratio indicates that less data were offloaded to the cloud, and privacy was better preserved. By enforcing the above ratio to remain tolerable, data privacy is promoted.