This paper presents the concept of Federated Learning (FL) of eXplainable Artificial Intelligence (XAI) models as an enabling technology in advanced 5G toward 6G systems and discusses its applicability to automated vehicles networking use case. On the one side, XAI permits improving user experience of the offered communication services by helping end-users trust (by design) that in-network AI functionality issues appropriate action recommendations. On the other side, FL ensures security and privacy of both vehicular and user data across the whole system. In this perspective, the paper also provides a detailed description of relevant 6G use cases, with focus on Vehicle-to-Everything (V2X) environments, for which FL of XAI models is expected to bring benefits, and a possible evaluation methodology involving online training based on real data from live networks. Impacts of the proposed approach (including standardization perspectives) consist in a better trustworthiness of operations, e.g., via explainability of Quality-of-Service (QoS) predictions, along with security and privacy-preserving management of data from sensors, terminals, users and applications.

Federated Learning of eXplainable AI models in 6G systems: Towards secure and automated vehicles networking

Alessandro Renda;Pietro Ducange;Francesco Marcelloni;Dario Sabella;Giovanni Nardini;Giovanni Stea;Antonio Virdis;
2022-01-01

Abstract

This paper presents the concept of Federated Learning (FL) of eXplainable Artificial Intelligence (XAI) models as an enabling technology in advanced 5G toward 6G systems and discusses its applicability to automated vehicles networking use case. On the one side, XAI permits improving user experience of the offered communication services by helping end-users trust (by design) that in-network AI functionality issues appropriate action recommendations. On the other side, FL ensures security and privacy of both vehicular and user data across the whole system. In this perspective, the paper also provides a detailed description of relevant 6G use cases, with focus on Vehicle-to-Everything (V2X) environments, for which FL of XAI models is expected to bring benefits, and a possible evaluation methodology involving online training based on real data from live networks. Impacts of the proposed approach (including standardization perspectives) consist in a better trustworthiness of operations, e.g., via explainability of Quality-of-Service (QoS) predictions, along with security and privacy-preserving management of data from sensors, terminals, users and applications.
Renda, Alessandro; Ducange, Pietro; Marcelloni, Francesco; Sabella, Dario; Filippou, Miltiadis C.; Nardini, Giovanni; Stea, Giovanni; Virdis, Antonio; Micheli, Davide; Rapone, Damiano; Gomes Baltar, Leonardo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1150519
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