## 5 Distributed Machine Learning Algorithms
### Distributed Learning for ISVN  
With these previous studies, it is inherently clear that distributed learning can play a huge role in enabling ISVN. Various distributed learning frameworks can perform efficient learning operations with the help of distributed vehicular data. In the case of latency-critical vehicular scenarios, it is important to perform ML training operations with limited resources. A pretrained ML model can often become outdated over time, incurring the issues such as model drift. Work performed in [112] highlights the importance of performing the continuous model training operations with new vehicular data. In particular, the authors have proposed the optimization approach for joint computation offloading and the FL training process. The trade-off between the FL training iterations vs. offloading performance is well explored. Additionally, vehicular mobility being one of the bottlenecks during vehicular processing operations is also taken into account. Such studies can surely motivate the vehicular community for adapting the online distributed learning operations by taking into account the resource constraints. Optimizing the distributed learning process based on the local vehicular environment can be a key idea for enabling the ISVN.  
Additionally, hierarchical learning approaches such as distributed FL process can also be useful while diving into the upcoming 6G world and corresponding intelligent vehicular services [118]. In such cases, the averaging process of FL can be distributed over multiple computing platforms/layers enabling the possibility of a large number of users to participate in the learning process with limited resource requirements and link failure probabilities.  
Integrating the meta-learning approaches in the traditional distributed learning process can also be one area to explore, especially for the ISVN case. Different meta-learning approaches can effectively reduce the training resource requirements and create high-quality ML models by exploring previous learning experiences.  
Thus adapting the distributed learning process based upon the limited vehicular resources, and vehicular mobility, considering the multilayered edge computing platforms (i.e., joint terrestrial and non-terrestrial networks) for distributing the training process, integrating the advanced learning techniques such as meta-learning into traditional learning approaches are some of the major future directions that can be useful for enabling the ISVN in coming days.  
The following Table 2 compares the different learning approaches in terms of their typical characteristics, especially for the vehicular user (VU) case.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Characteristics** & **Centralized Learning** & **Decentralized Learning** & **Collaborative Learning** & **Federated Learning** \\ \hline Learning Entity & Centralized Server & Distributed Servers/Devices & Distributed Servers & Distributed Servers & On Device (VUs) \\ \hline Scalability & Low & High & High & Very High \\ \hline Energy Cost (VU Side) & Low & High & High & High \\ \hline Latency & High & Low & Low & Medium \\ \hline VUs Sensitive Data Privacy & Medium & Limited & Limited & High \\ \hline \multirow{4}{*}{Pros} & Training Fairly Complex & \multirow{4}{*}{Training Complex ML with limited number of layers} & \multirow{4}{*}{Training Complex ML Models} & Training Models with Limited Complexity & Training Models with Limited Complexity \\ \cline{1-1} \cline{3-5}  & High Resource & High Resource & High Resource Requirements, larger & Requirements, Server/Device Selection with Proper Data & VUs mobility, Proper Device/Server Selection, Model Accuracy \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distributed ML models and corresponding characteristics.