This thesis presents the development of a distributed platform for cybersecurity data collection and analysis, leveraging machine learning (ML) models to detect potential cyberattacks. The platform comprises multiple nodes, which can function as either darknets or honeypots. These nodes capture network data, which is then used to train models using federated learning (FL). Federated learning enables the distributed training of neural networks, achieving results comparable to centralized approaches while preserving data privacy. Each node trains a local model using its own dataset, and only the model weights are shared and aggregated on a central server. The training process is built around the Flower Federated Learning framework.