PICRAH 4.0 – Adaptive Robotics and Federated Learning

Intelligent and cyber-secure platform for adaptive optimization in simulated operation of heterogeneous autonomous robots

Funded by

CDTI – Spain, national

Institution

Ceit Research Center

Role

Research collaborator

Date

Joined in November 2026

So far I have contributed the state of the art on federated learning applied to autonomous robots through adaptive optimization in the simulated operation of heterogeneous autonomous robots. I have also contributed to data-cybersecurity protocols for network monitoring and traffic management, and developed unsupervised and supervised machine-learning algorithms to detect network-traffic anomalies, including an intrusion detection system (IDS) based on spectral clustering that detects four attack types in industrial-robot network traffic.

I co-authored the article “Anomaly Detection Using Machine Learning for Robotics Environments on 5G Networks” in the journal Technologies, which describes the deployed architecture and the anomaly-detection models with process data. In addition, I have developed AI-powered intrusion detection for MQTT-based IoT networks.

More information about my participation in this project: projects on LinkedIn. Related project: ADILAGUN – distributed AI for industry.