## 3 Research Questions and Communication Efficient Methods
### RQ2-How Can Communication Be More Efficient in an FL Environment?
#### 3.2.5 Compression Schemes  
In addition to the local updating methods, some ML compression techniques may also be implemented to reduce the total number of rounds between the centralized server and the devices connected to the federated network. Compression schemes such as quantization and sparsification that are sometimes integrated with the aggregation algorithms can sometimes provide reasonable training accuracy results while, at the same time, also reducing the overall communication cost. The authors in [28] list the objectives set for compression schemes and federated learning, such as:  
* Reducing the size of the object / ML model from the clients to the server, i.e., that used to update the overall global model. * Reducing the size of the global model that is shared with the clients on the network, i.e., the model on which the clients start local training using the available data. * Any changes that are made to the overall training algorithm that make training the global training model more computationally efficient. From the list of objectives, **A** could have the highest effect on the overall running time; therefore, reducing it would directly result in reducing the overall communication cost. The clients on a federated network generally have a slower upload bandwidth compared to the download bandwidth. Therefore, compressing the ML models and potentially reducing the uplink/downlink exchanges could result in reducing the communication cost [39]. In this subsection, compression schemes towards achieving communication efficiency in a federated environment are presented. First, sharing the research conducted towards compression schemes is discussed. Then, conducted works on concerned methods of sparsification and quantization are covered. _Sparsification:_ Sparsification is a process of improving the model by removing unnecessary information, mainly when it comes to deep learning approaches. It aims to provide a faster and more efficient model that could enhance communications within specific techniques in ML and FL. This technique is a type of communication technology that can be implemented in an FL setting to compress the model when communicated across the server. Herein, the conducted research towards making integrating sparsification in an FL environment is discussed. The authors in [55] present a sparse ternary compression (STC) method that not only adheres to the compression requirements of an FL setting and environment but also provides compression for both upstream and downstream communications. They run an analysis of FL models over various datasets and architectures. In their analysis, they conclude that some factors of an FL environment are hugely dependent on the convergence rate of the averaging algorithm. Moreover, the authors deduce that training on non-iid small portions of data or when only a subset of clients participate in communication rounds can reduce the convergence rate. However, the proposed model of STC is a protocol that compresses communication via sparsification, ternarization, error accumulation, and the optimal Golomb encoding. The robust technique provided by the authors in [55] converges relatively faster when compared to other averaging algorithms, such as FedAVG, over both factors of non-iid data and a lower number of iterations that are communicated. The proposed method is also highly effective when the communication bandwidth is constrained. Sparse client participation is another challenge that needs to be overcome in an FL environment; authors have introduced a FetchSGD algorithm that can help towards achieving this [82]. FetchSGD overall aids with the communication constraints of an FL environment by compressing the gradient that is based on the client's local data. The data structure count sketch [83] is used to compress the gradient before it is uploaded to the central server. The count sketch is also used for error accumulation.