Enabling federated learning of explainable AI models within beyond-5G/6G networks

Corcuera Bárcena, José Luis; Ducange, Pietro; Marcelloni, Francesco; Nardini, Giovanni; Noferi, Alessandro; Renda, Alessandro; Ruffini, Fabrizio; Schiavo, Alessio; Stea, Giovanni; Virdis, Antonio

doi:10.1016/j.comcom.2023.07.039

Cited by 9 publications

(1 citation statement)

References 29 publications

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“…The need for trust in the context of AI for future B5G/6G wireless networks is addressed in (Bárcena et al, 2023). It proposes a Federated Learning-as-a-Service (FLaaS) framework that integrates FL with XAI models to enhance trustworthiness and privacy in collaborative model training.…”

Section: Related Workmentioning

confidence: 99%

Balancing Privacy and Explainability in Federated Learning

Teixeira,

Almeida,

Rodrigues

et al. 2023

Preprint

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As we advance towards future generations of communication networks, such as 6G, Artificial Intelligence (AI) and Machine Learning (ML) will assume an increasingly pivotal role in network optimization, management, and operation. In this context, the pursuit of reliability in ML models has emerged as a highly active area of research. Explainable AI (XAI) is an essential tool to unravel the underlying mechanisms of network behaviour, enabling a deeper understanding of the decisions made by black-box models in future-generation networks. This paper investigates the impact of Federated Learning (FL) on explainability by evaluating the correlation between the feature importance derived from models trained using four different FL approaches and the model trained using a conventional single-host approach. The analysis utilizes two publicly available datasets, two XAI metrics, and two correlation metrics. The findings reveal a high correlation between FL and single-host trained models in three of the four FL approaches, suggesting that some FL approaches failed to capture similar patterns.

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Section: Related Workmentioning

confidence: 99%

Balancing Privacy and Explainability in Federated Learning

Teixeira,

Almeida,

Rodrigues

et al. 2023

Preprint

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Federated Learning of XAI Models in Healthcare: A Case Study on Parkinson’s Disease

Ducange,

Marcelloni,

Renda

et al. 2024

Cogn Comput

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Artificial intelligence (AI) systems are increasingly used in healthcare applications, although some challenges have not been completely overcome to make them fully trustworthy and compliant with modern regulations and societal needs. First of all, sensitive health data, essential to train AI systems, are typically stored and managed in several separate medical centers and cannot be shared due to privacy constraints, thus hindering the use of all available information in learning models. Further, transparency and explainability of such systems are becoming increasingly urgent, especially at a time when “opaque” or “black-box” models are commonly used. Recently, technological and algorithmic solutions to these challenges have been investigated: on the one hand, federated learning (FL) has been proposed as a paradigm for collaborative model training among multiple parties without any disclosure of private raw data; on the other hand, research on eXplainable AI (XAI) aims to enhance the explainability of AI systems, either through interpretable by-design approaches or post-hoc explanation techniques. In this paper, we focus on a healthcare case study, namely predicting the progression of Parkinson’s disease, and assume that raw data originate from different medical centers and data collection for centralized training is precluded due to privacy limitations. We aim to investigate how FL of XAI models can allow achieving a good level of accuracy and trustworthiness. Cognitive and biologically inspired approaches are adopted in our analysis: FL of an interpretable by-design fuzzy rule-based system and FL of a neural network explained using a federated version of the SHAP post-hoc explanation technique. We analyze accuracy, interpretability, and explainability of the two approaches, also varying the degree of heterogeneity across several data distribution scenarios. Although the neural network is generally more accurate, the results show that the fuzzy rule-based system achieves competitive performance in the federated setting and presents desirable properties in terms of interpretability and transparency.

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A Comprehensive Review on Machine Learning-based Approaches for Next Generation Wireless Network

Paul

2024

SN COMPUT. SCI.

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Enabling federated learning of explainable AI models within beyond-5G/6G networks

Cited by 9 publications

References 29 publications

Balancing Privacy and Explainability in Federated Learning

Balancing Privacy and Explainability in Federated Learning

Federated Learning of XAI Models in Healthcare: A Case Study on Parkinson’s Disease

A Comprehensive Review on Machine Learning-based Approaches for Next Generation Wireless Network

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