## 4 Results and Discussion
### Discussion
Furthermore, introducing a middle layer between the client and server can help overcome the non-IID data challenge without extra computation at the client or the server. To answer RQ3, the most widely used techniques to provide communication efficiency in FL are shown in Figure 12. Based on the selected studies and findings outlined in this work, compression techniques are a research hotspot in FL for providing efficient communication; compression techniques of particular importance are quantization and sparsification, which are used in 27% (20 studies) and 15% (11 studies) of the studies, respectively. These techniques reduce the number of bits transmitted by compressing the model. Quantization in FL: Quantization is used to reduce the number of bits that represent a value; it is essential to set the quantization level carefully to avoid damaging the model and prevent the loss of useful information. For instance, the studies conducted in [143; 145] used an adaptive level of quantization to minimize the error bound. In contrast, [PERSON]. [152; 156] used a 1-bit quantization scheme to quantize the local model to 1-bit data, while the work in [184] used ternary quantization that quantizes the value into one of three values (\(-\)1,0,1). Sparsification in FL: Sparsification schemes are used to reduce the number of bits transmitted. Different studies used different methods to select these bits; for example, the work in [141] sets the non-important weight update elements to zero, while the works in [153; 165] use a top-k selection-based gradient compression scheme. In addition, [PERSON]. [155] uses block sparsification by dividing the local gradient vector into sub-vectors and then dropping some gradient entries with small magnitudes at each sub-vector. These compression techniques require clients to compress the model without losing any vital information and share the compressed model with the server, as losing the vital information may affect the global model performance and convergence speed. However, most of these techniques are applied  
Figure 20: The classification of the techniques for solving the non-IID data challenge in federated learning. on the client side, which means that clients need to be capable of training the model and applying a compression technique, and this can be challenging due to the limited resources of clients. We can classify the top techniques into techniques that aim to reduce the number of bits transmitted (reducing the model size) and techniques that aim to reduce the number of updates, as shown in Figure 21. Quantization, sparsification, and knowledge distillation techniques focus on reducing the number of bits shared to provide efficient communication. The client selection, select model updates, over-the-air computation, clustering, periodic model averaging, asynchronous, and two-level aggregation techniques aim to reduce the number of updates between the server and the clients. The client selection scheme is used to select the clients that can contribute more to enhance the global model, which results in a reduction in the communication rounds [147; 185; 191]. Using asynchronous communication can enhance the global model performance by allowing the aggregation of the received model without waiting for all clients [146; 171; 211]. Select model update is a technique that uploads the trained model that can help model coverage and ignore irrelevant updates [157; 189]. Over-the-air computation [179; 197] is a technique used by exploiting the waveform superposition property, which directly obtains the aggregated model. This technique can provide faster and more communication-efficient training in federated learning. Clustering is where similar clients are clustered together and a representative is selected to share the updated model [172; 177; 185]. Periodic model averaging [198; 209] is where the local model is uploaded periodically to reduce the number of updates.