# Rebirth of Distributed AI--A Review of eHealth Research
## 5 Federated Learning Tutorial
### Types of Federated Learning
#### 5.2.1 Intra-Features and Inter-Regions Federated Learning  
In this category, the datasets share the same feature set but different samples. This category focuses on applications that involve clients from different organizations, geographically distributed, and different technologies. Data are generated locally and remain decentralized. Each client stores its own data and cannot access the data of other clients. Data are not independently or identically distributed. Although the central server carries out all the organization, it just gets the hyper parameter values from the clients and has no access to raw data. Suitable communication bit-pipes are used for connectivity between the clients and a central server. Each client may participate in all rounds of the computation. Major application domains for this type of federated learning are: health, finance, autonomous driving, mobile network, etc.  
For instance, let say there are a number of hospitals in a city that collaboratively want to develop a prediction model for COVID-19 or for breast cancer cases. Each hospital stores a different set of patient records, and the overlapping of samples is very low. Nevertheless, all hospitals store the records using the same attributes or features set. Federated learning in this case works with the same feature set but different sample sets. See Figure 7. Along similar lines, in an autonomous driving scenario, consider typical geographically distant road segments with road segment-specific environmental variables. Assume that the OEMs need to learn about the planning of autonomous vehicles, and road infrastructure providers wish to learn about traffic intensity in those road segments. Although the vehicles in the two segments are different, they have a features set in common.  
This type of federated learning can be defined as:  
\[\mathcal{X}_{i}=\mathcal{X}_{j},\mathcal{Y}_{i}=\mathcal{Y}_{j},\mathcal{I}_{i }\
eq\mathcal{I}_{j},\]  
\(\forall\mathcal{D}_{i}\), \(\forall\mathcal{D}_{j}\), \(i\
eq j\)  
Figure 7: Intra-features and inter-regions FL.  
The authors in [39] used federated learning for the prediction of insurance issues, [40] looked into electronic healthcare records mining, ref. [41] looked into application of learning in pharmaceuticals discovery, ref. [42; 43] used federated learning in medical data segmentation, and the authors of [44] exploited the features of federated learning smart manufacturing. In the recent past, Google collaborated with an Android-based horizontal federated learning solution [57]. An Android user updates the parameters locally and shares them with the Android cloud. Similarly, authors in [58] proposed a deep learning scheme where the stakeholders train the model locally and share a subset of parameters with the global model. An aggregation scheme to ensure user privacy in a federated learning framework is discussed in [59]. Authors in [60] contributed with a multitask federated learning that enables multiple sites to finish separate tasks while ensuring privacy and sharing the required information for learning. A client-server structure is proposed in [57], where the federated learning allows the models to be built at client devices and collaborates at the server to build a global federated model. The contribution to improving the communication bit-pipes and the relevant costs of enabling federated learning is discussed in [61]. A similar approach relevant to communication is proposed by the authors of [62], who focused on Deep Gradient Compression to reduce the communication bandwidth requirements in large-scale distributed training.