## 3 Research Questions and Communication Efficient Methods
### RQ2-How Can Communication Be More Efficient in an FL Environment?
#### 3.2.1 Local Updating  
Distributed ML that exists in data centers has become popular when integrating mini-batch optimization methods. However, there are still some constraints and limitations on flexibility. This form of batch optimization has constraints over a more federated setting that includes both communication and computation challenges [48; 56]. Local updating in FL attempts to train ML models on each device with their own data instead of sharing data captured from devices with the ML model to be trained together. Consequently, each device is trained locally and sends its own updates to the decentralized server. Then, the updates will be aggregated in order to create the global model. However, the overall objective of local updating is mostly focused on the aforementioned point (2), i.e., fitting and training the ML model locally on-device using the data generated by those devices. However, computing locally over a singular communication round and then applying those updates to the central server is not that efficient due to unexpected dropouts of devices or synchronization latency due to poor network connectivity [57]. Authors in [6] also highlight that it is very important to have a form of flexibility when considering the optimization method: flexibility towards client participation and local updates. There are a few ways to achieve this flexibility and, in turn, improve the overall communication efficiency. Techniques such as primal-dual methods can offer a form of flexibility, where local devices can use local parameters to train the global model locally to find arbitrary approximations [58]. This leveraging and breaking down of the overall objective of the global model can allow for problems of training to be solved in parallel over each communicated round [56]. In this subsection, some methods towards communication efficiency that have come to the surface regarding local updating techniques are reviewed. Figure 6: Ref. [55] illustrates a complete round of distributed stochastic gradient descent (SGD) model. In (**a**), clients on the federated network synchronize with the server and download the global ML model. In (**b**), the global model is trained by each client on their local data, adjusting the weighted average of the ML model as per. In (**c**), the new average achieved by each client is updated onto the server, where each update is used to obtain a new weighted average for the global model. In an FL setting, the data can be distributed unevenly across devices and may not always be identical. This can be considered as due to the data across the FL environment being distributed in a non-independent and identically distributed manner, i.e., non-iid. Having the datasets in a non-iid state could challenge the testing accuracy of an FL model [57]. The testing accuracy of a locally trained ML model is important as it will contribute to the global model. Therefore, having an accuracy of a local model that performs poorly could also deteriorate the overall performance of the global model. In [59], the authors introduce their way of combining FL with hierarchical clustering (HC) techniques to reduce the overall communication rounds within the FL protocol. The proposed clustering step can cluster and separate the clients by the similarity of the local updates to the global model. These similarities are the weights that are achieved by locally updating the local model on each device. Upon testing and comparing their integrated FL and HC technique, the authors concluded that the communication rounds are reduced as per a comparison on the Manhattan distance metric [59; 60]. Due to the large number of connected devices that attempt to communicate their locally updated model parameters with the central server--step (2)--this could cause a communication bottleneck due to bandwidth in some instances [61]. The authors in [61] introduce a federated periodic averaging and quantization framework (FedPAQ) as a new way to counter this problem.