## 3 Research Questions and Communication Efficient Methods
### RQ2-How Can Communication Be More Efficient in an FL Environment?
#### 3.2.2 Client Selection  
Client selection is an approach that plays a key role for FL environments. This technique determines which devices should be trained and which parameter could be specified to be aggregated for the local updates. It can be implemented to make communication more efficient by reducing the costs and restricting the number of participating devices so that only a fraction of the parameters are updated over the communication round [39]. In this subsection, conducted works on the client selection and its benefits towards improving the communication overload are presented. An approach introduced by the authors in [66] tackles the limitations that are present in communication over an FL setting. They create a multi-criteria client selection model, called FedMCCS, for IoT devices over an FL setting. The FedMCCS approach considers all of the specifications and network conditions of the IoT device for client selection. Factors such as the CPU, memory, energy, and time are all considered for the client resources to determine whether the client would be able to participate in the FL task. The FedMCCS approach considers more than one client and, over each round, the number of clients participating is increased. From their analysis, FedMCCS, compared to other approaches, can outperform by reducing the total number of communication rounds in order to achieve a reasonable accuracy. Another factor that is a vital property of FL, according to the authors in [67], is the varying significance of learning rounds. This comes to the surface when the authors realize that the learning rounds are temporally interdependent but have varying significance when achieving the final learning outcome. This conclusion comes from running numerous data-driven experiments; the authors create an algorithm that utilizes the wireless channel information but can achieve a long-term performance guarantee; the algorithm provides a desired client selection pattern adapted to network environments. The authors in [68] introduced a framework that specifically tackles the challenges when it comes to client selection. The authors called their framework FedCS. Their goal is that, when it comes to client selection in a standard FL setting, it can sometimes be a random selection of clients (or devices). However, with their approach of client selection, they break it into two steps. First, a resource request, where client information, such as the state of the wireless channel and computational capacities, etc., are requested and shared with the central server. Second, in order to estimate the time required, distribution and scheduled update and upload steps are taken. With their framework, the overall approach of the client selection is to allow the server to aggregate as many clients within a certain time frame, knowing that factors, such as data, energy, and computational resources, i.e., used by the devices, could better meet the requirements of a training task and possibly affect the energy consumption and bandwidth cost. To better assess whether a client can sufficiently participate or not, a resource management algorithm introduced by the authors in [69] adopts a deep-Q learning algorithm that can allow the servers to learn and make optimal decisions without having to know prior network knowledge. Their mobile crowd machine learning (MCML) algorithm addresses the constraints present in mobile devices, reducing the energy consumed and making the training and communication time more efficient. Authors run their analysis, providing a method that practices biased client selection strategies [70]. As per their analysis, the selection of bias can affect the convergence speed; the bias of their client selection works towards clients that have a higher local loss and achieve a faster error convergence. With this knowledge, the authors create a communication and computation-efficient framework called power-of-choice. Their framework has the agility to trade off between the bias and convergence speed.