Pedro Miguel Sanchez Sanchez

arXiv2025

Assessing the Sustainability and Trustworthiness of Federated Learning Models

Artificial intelligence (AI) increasingly influences critical decision-making across sectors. Federated Learning (FL), as a privacy-preserving collaborative AI paradigm, not only enhances data protection but also holds significant promise for intelligent network management, including distributed monitoring, adaptive control, and edge intelligence. Although the trustworthiness of FL systems has received growing attention, the sustainability dimension remains insufficiently explored, despite its importance for scalable real-world deployment. To address this gap, this work introduces sustainability as a distinct pillar within a comprehensive trustworthy FL taxonomy, consistent with AI-HLEG guidelines. This pillar includes three key aspects: hardware efficiency, federation complexity, and the carbon intensity of energy sources. Experiments using the FederatedScope framework under diverse scenarios, including varying participants, system complexity, hardware, and energy configurations, validate the practicality of the approach. Results show that incorporating sustainability into FL evaluation supports environmentally responsible deployment, enabling more efficient, adaptive, and trustworthy network services and management AI models.

cs.CY↗

arXiv2024

CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation

Recent research has shown that the integration of Reinforcement Learning (RL) with Moving Target Defense (MTD) can enhance cybersecurity in Internet-of-Things (IoT) devices. Nevertheless, the practicality of existing work is hindered by data privacy concerns associated with centralized data processing in RL, and the unsatisfactory time needed to learn right MTD techniques that are effective against a rising number of heterogeneous zero-day attacks. Thus, this work presents CyberForce, a framework that combines Federated and Reinforcement Learning (FRL) to collaboratively and privately learn suitable MTD techniques for mitigating zero-day attacks. CyberForce integrates device fingerprinting and anomaly detection to reward or penalize MTD mechanisms chosen by an FRL-based agent. The framework has been deployed and evaluated in a scenario consisting of ten physical devices of a real IoT platform affected by heterogeneous malware samples. A pool of experiments has demonstrated that CyberForce learns the MTD technique mitigating each attack faster than existing RL-based centralized approaches. In addition, when various devices are exposed to different attacks, CyberForce benefits from knowledge transfer, leading to enhanced performance and reduced learning time in comparison to recent works. Finally, different aggregation algorithms used during the agent learning process provide CyberForce with notable robustness to malicious attacks.

cs.CR↗