# Rebirth of Distributed AI--A Review of eHealth Research
## 5 Federated Learning Tutorial  
### An Overview  
Federated learning is a machine learning variant, where multiple clients train a model collaboratively and are orchestrated by a centralized server. In this setting, the training data are decentralized. [PERSON] in [57] coined the term \"Federated Learning\" for the first time in 2016. Although federated learning initially focused on edge devices and mobile applications, the interest in applying FL to other application domains has greatly increased \begin{table}
\begin{tabular}{c c c} \hline \hline
**Application** & **Ref.** & **Focus** \\ \hline \multirow{8}{*}{EHR-based (e.g., medical images) prediction models} & [37] & EHR application to predict hospitalizations for patients with heart diseases \\ \cline{2-3}  & [26] & EHR application for medical whole-brain segmentation \\ \cline{2-3}  & [38] & EHR-based model \\ \cline{2-3}  & [39] & federated learning for prediction of insurances \\ \cline{2-3}  & [40] & electronic healthcare records mining \\ \cline{2-3}  & [41] & application of learning in pharmaceuticals discovery \\ \cline{2-3}  & [42; 43] & use federated learning in medical data segmentation \\ \cline{2-3}  & [44] & utilizes the features of federated learning in smart manufacturing and healthcare \\ \cline{2-3}  & [45] & decentralized FL approach working on medical images \\ \cline{2-3}  & [26] & BrainTorrent: a FL framework to train a complex fully CNN in a decentralized fashion using whole-brain image segmentation \\ \hline \multirow{8}{*}{Telesurgery its design requirements} & [46] & Tactile Robotic for Telesurgery \\ \cline{2-3}  & [47] & Telesurgery and its enabling technologies \\ \cline{2-3}  & [48] & Communication requirements for telesegury \\ \cline{2-3}  & [49] & Tactile-based Telesurgery \\ \cline{2-3}  & [30] & Communication requirements for telesegury \\ \cline{2-3}  & [50] & Holography for telesurgery \\ \cline{2-3}  & [51] & Holography applications toward medical field \\ \hline \multirow{8}{*}{IoMT and wearable devices and their design requirements} & [52] & survey of commercially available wearables \\ \cline{2-3}  & [53] & a survey about IoMT \\ \cline{1-1} \cline{2-3}  & [54] & ECG monitoring systems \\ \cline{1-1} \cline{2-3}  & [28] & communication requirements for IoMT to be provided by 6G \\ \cline{1-1} \cline{2-3}  & [29] & communication requirements for IoMT provided by 5G \\ \cline{1-1} \cline{2-3}  & [55] & FedHome: a cloud-edge based federated learning framework for in-home health monitoring. \\ \cline{1-1} \cline{2-3}  & [56] & FedHealth: federated transfer learning framework for wearable healthcare. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Articles about FL applications in Healthcare domain. in the recent past. Domains include autonomous driving, eHealth, Industry 4.0, mobile operator networks, etc. Inspired by different versions of federated learning in literature, we came up with the following definition that we believe captures the crux of the idea. **Definition 1**. : _Federated learning is a setting of machine learning allowing multiple entities (may also be termed as clients) to collaborate in solving problems, where the orchestration is carried out by a service provider or a central server. Clients do not need to send the local data to the central server; instead, a model is trained locally at clients and the hyper parameters are exchanged with the server. The process may be repeated until the desired accuracy is achieved._  
Alternatively, federated learning may be defined as:  
**Definition 2**. : _Let there be \(\mathcal{N}\) number of data owners, which is the set of distinct elements \(\{F_{1},\dots F_{N}\}\). All the elements of the set train a machine learning model on their local data \(\{D_{1},\dots,D_{N}\}\). On the contrary to the classical (central server) method where all data is collected by the centralized server \(\mathcal{D}=D_{1}\cup\dots\cup D_{N}\) to train a model \(\mathcal{M}_{c}\), in federated learning the data owners collaboratively train a model \(\mathcal{M}_{f}\).