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Trustworthy AI for Next Generation Networks: the Fed-XAI innovative paradigm from the Hexa-X EU Flagship Project

Authors: Ducange, Pietro; Marcelloni, Francesco; Micheli, Davide; Nardini, Giovanni; Renda, Alessandro; Sabella, Dario; Stea, Giovanni; +1 Authors

Trustworthy AI for Next Generation Networks: the Fed-XAI innovative paradigm from the Hexa-X EU Flagship Project

Abstract

This work presents the joint research activities on AI in and for 6G carried out by University of Pisa, Intel Corporation Italia s.p.a. and Telecom Italia s.p.a., within the Hexa-X EU project. Specifically, we focus on Federated Learning of Explainable Artificial Intelligence (Fed-XAI), which has been recently awarded as key innovation by the EU Innovation Radar. We present the main recent achievements, that can be summarised in algorithms for generating federated XAI models in a privacy-preserved environment, a communication framework for Federated-Learning-as-a-Service and orchestration algorithms of federated learning participants. Finally, we discuss a proof of concept, that showcases the aforementioned Fed-XAI components.

First published here: https://www.ital-ia2023.it/media/papers/DucangeEtAll.pdf

Country
Italy
Keywords

Federate Learning, Explainable AI; Federate Learning; Simu5G, Explainable AI, Federated Learning, Explainable AI, Simu5G, Simu5G

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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