## 3 Research Questions and Communication Efficient Methods
### RQ2-How Can Communication Be More Efficient in an FL Environment?
#### 3.2.5 Compression Schemes
In contrast to typical FL models, where the global model from a central server is downloaded by all devices and then subsequently updated and so and so forth, the authors from their analysis discovered that applying quantization techniques to the global model can help towards making communication more efficient. The lossy FL algorithm (LFL) created by the authors quantizes the global model before it broadcasts and shares it with the devices. The local updates that take place on the device are uploaded on the global central server and are also quantized. The FL environment is hugely dependent on the bandwidth, and the authors in [89], for their analysis, study how well the quantized compressed global model performs in order to provide an estimate for the new global model from the local updates of the devices. They compare their results with another analysis of a lossless standard approach of FL and conclude that their LFL and the technique of quantizing global model updates provide a significant reduction in the communication load. Traditionally, compression algorithms are trained for a setting where there is a high-speed network, such as data centers [90], but, in an FL setting, these algorithms might not be very effective directly. To further tackle the communication bottleneck that is created due to the network and the other aforementioned reasons, another quantization compression technique is proposed by [91]. To achieve a trade-off between communication efficiency and accuracy, the authors in [91] propose a hyper-sphere quantization (HSQ) framework. HSQ can reduce the communication cost per iteration, which is ideal for an FL environment. The framework utilizes vector quantization techniques that show an effective approach towards achieving gradient compression and simultaneously not compromising regarding convergence accuracy. In another study, the authors in [92] suggest that the communication channel and transfer of model parameters between the users to the central server has a throughput that can be typically constrained. The authors, while conducting their research, encountered that alternative methods used to aid with this could provide a dominant distortion of results. This leads [92] to create a quantization method that is more efficient towards facilitating the model transfer in an FL setting. Utilizing quantization theory methods, the authors design quantizers that are suitable for distributed deep network training. Understanding the requirements that are needed for a quantization FL setting, the authors can propose an encoding-decoding strategy. Their proposed scheme shows potential for an FL setting, as it performs relatively well compared to previously proposed methods. Furthering their research towards quantization theories, the authors in their review approach it by identifying unique characteristics regarding the conveyed trained models over rate-constrained channels [93]. The authors propose a universal vector quantization technique for FL: a quantization technique that would be suitable for such settings, calling it UVeQFed. For their research, the authors demonstrate that combining universal vector quantization methods can yield a system where the compression of trained models induces only a minimum distortion. Analyzing the distortion further, the authors determine that the distortion is reduced substantially as the number of users grows. The authors in [94] introduce a hierarchical quantized federated learning technique that can leverage a client-edge-cloud network hierarchy and quantized model updates. Their heir-local-QSGD algorithm performs partial edge aggregation and quantization on the model updates, which can result in improving the communication efficiency in an FL environment. A summary table of the works presented in this section is given in Table 2.