To simulate a real-world scenario, VMs were set up and configured to act as clients in a FL system. This simulation allowed for the exploration of diverse data distributions, data processing capabilities, and communication constraints that might arise in a real deployment. The following table summarizes the characteristics of such a test bed.

Another crucial factor in designing the testing set up is the ML framework to be used. To this end, PyTorch was selected as the primary ML framework. The flexibility of PyTorch allowed for the implementation of complex models and easy customization to meet specific project requirements. Also, the availability of pre-trained models and a vast collection of built-in functions expedited the development process and enabled focus on the core aspects of the project. Another pivotal factor is PyTorch’s ability to leverage GPU for hardware acceleration, which is crucial for training models on distributed data in FL environments. Its integration with CUDA and optimisation optimization for GPU computing make it a pragmatic choice for applications requiring high performance. Lastly, PyTorch was chosen for its adaptability within the existing development environment, including its compatibility with Docker and '''embedded devices based on the ARM64 (AArch64) architecture'''. PyTorch’s adaptability and support for ARM64 architecture were key factors in this decision. This interoperability has facilitated the integration of the framework into the research and development environment.