In this case, cloud parties consist of two embedded devices or virtual machines acting as clients, and a notebook serving as the server. This configuration facilitates a distributed learning approach, enabling the clients to process their data locally while contributing to the model’s training. The server coordinates the learning process and aggregates the updates from the clients to improve the global model. This setup ensures a decentralized and privacy-preserving approach to ML, as the data remains on the clients’ devices, and only the model updates are shared during the training process. Leveraging embedded devices as clients enables the inclusion of resource-constrained devices in the FL ecosystem, making the framework more versatile and applicable to a wide range of scenarios. The A notebook acting as the server provides a centralized point of coordination and ensures smooth communication and collaboration between the clients, making the FL process efficient and effective in leveraging distributed resources for improved model performance. Of course, this environment is more complicated to set up, but it better simulates real configurations. ===== Virtual environment =====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. ===== Embedded Environment =====