# Rebirth of Distributed AI--A Review of eHealth Research
## 8 Selected IoMT-Based Health Applications
### Tele-Surgery or Autonomous Surgery  
\"Democratization of skillful surgeons\" could be the best term to describe telesurgery, especially with the increased population and the shortage of skilled surgeons. Telesurgery basically allows surgeons to operate on patients who are distantly located via a robotic onsite surgeon. This technology will increase patient access to highly skilled surgeons by  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline  & **Clients Selection** & **Model Broadcast** & **On-Client Update** & **On-Server Aggregation** & **Synchronization Scheme** & **Approach** \\ \hline IFCA [106] & & & \(\chi\) & & & the client estimates the cluster membership from the models sent. \\ \cline{2-6}  & & & \(\chi\) & & the server aggregates the received models per cluster \\ \hline FedMax [89] & X & & & & & introduce a similarity-based worker selection approach, which chooses the most effective workers with least dataset similarity (e.g., more non-IID). As data is not accessible by the central server in federated learning, FedMax smartly implies dataset similarity from workers’ updates. \\ \hline  & & & & \(\chi\) & realize a relaxed synchronization communication scheme with a workload balancing mechanism by taking heterogeneous computation capacities of workers into account. \\ \hline FAVOR [87] & X & & & & dynamic device selection using DRL \\ \hline VKN [90] & X & & & an orchestration method to intelligently select the proper vehicles required to train a model. The heart of their proposed orchestration method is a Vehicular Knowledge Networking framework \\ \hline [86] & & & X & sending only relevant updates for aggregation \\ \hline [113] & & & X & only “fine” local models are sent for aggregation \\ \hline [114] & X & & & & To further reduce communication, they group the CSs into clusters before applying the FL algorithm \\ \hline \multicolumn{6}{p{56.9 pt}}{**Communication**} \\ \hline FedPaQ [84] & X & X & X & combine both periodic averaging and model compressing using quantization \\ \hline FADL [70] & X & & & train the first layers of the neural network model using data from all sources in a federated learning manner and the other parts locally using local data from specific data sources \\ \hline Split Learning [71] & X & X & & split the layers of the network between the client and the server, where the server does the heavy load \\ \hline PRCL [72] & X & & & split the model into three parts and outsourcing the computationally heavy part to the cloud and using homomorphic encryption \\ \hline FetchSGD [74] & X & X & X & use Count Sketch to compress model \\ \hline FedPaQ [84] & & X & X & X & combine both periodic averaging and model compressing using quantization \\ \hline [38] & & X & & & perform local updates for several iterations and then enables nodes to communicate with each other \\ \hline PFL-IU [86] & & & X & & remove irrelevant updates instead of compressing them \\ \hline [115] & X & & & & Active Learning for devices selection \\ \hline [109] & & X & & Active learning to speed up model convergence \\ \hline \multicolumn{6}{p{56.9 pt}}{**Privacy and Security**} \\ \hline [19] & & & X & & identify malicious participants via their model updates \\ \hline [98] & & X & & use differential privacy to perturb the gradients as a defense layer against reverse engineering \\ \hline [99] & & X & & mitigate the noise introduced by differential privacy \\ \hline \multicolumn{6}{p{56.9 pt}}{} \\ \hline [116] & X & X & X & & adopt Multi-Party Computation (MPC) to achieve privacy-preserving model aggregation for FL \\ \hline \multicolumn{6}{p{56.9 pt}}{} \\ \hline \([[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[ [[[[[[[[[[[[[[ [[[[[[[[[[ [[[[[[[[[[[[ [[[[[[[[[[ [[[[[[[[[[[[[ [[[[[[[[[[[[[[[[ [[[[[[[[[[[[[[[[[[[[[ [[[[[[[[[[[[[eliminating geographical barriers and saving time and cost.