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This research was partially supported by MCIN/AEI/10.13039/501100011033 (PhD grant PRE2019-089210 and project PID2021-123637NB-I00 "CURLING"), INCIBE (project "HERMES" and INCIBE-URV cybersecurity chair), the European Commission (project H2020-871042 "SoBigData++"), the Government of Catalonia (ICREA Acad`emia Prizes to J. Domingo-Ferrer and to D. Sanchez, and grant 2021 SGR 00115), PNRR -M4C2 -Investimento 1.3, Partenariato Esteso PE00000013 -"FAIR -Future Artificial Intelligence Research" -Spoke 1 "Human-centered AI", funded by the European Commission under the NextGeneration EU programme, PNRR -"SoBigData.it -Strengthening the Italian RI for Social Mining and Big Data Analytics" -Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency (HaDEA). Neither the European Union nor the granting authority can be held responsible for them. Grant Agreement no. 101120763 -TANGO, ERC-2018-ADG G.A. 834756 XAI: Science and technology for the eXplanation of AI decision making (https://xai-project.eu/index.html).

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GLOR-FLEX: Local to Global Rule-based EXplanations for Federated Learning

Publicated to:2024 Ieee International Conference On Fuzzy Systems, Fuzz-Ieee 2024. - 2024-01-01 (), DOI: 10.1109/FUZZ-IEEE60900.2024.10611878

Authors: Haffar, Rami; Naretto, Francesca; Sanchez, David; Monreale, Anna; Domingo-Ferrer, Josep

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Abstract

The increasing spread of artificial intelligence applications has led to decentralized frameworks that foster collaborative model training among multiple entities. One of such frameworks is federated learning, which ensures data availability in client nodes without requiring the central server to retain any data. Nevertheless, similar to centralized neural networks, interpretability remains a challenge in understanding the predictions of these decentralized frameworks. The limited access to data on the server side further complicates the applicability of explainers in such frameworks. To address this challenge, we propose GLOR-FLEX, a framework designed to generate rule-based global explanations from local explainers. GLOR-FLEX ensures client privacy by preventing the sharing of actual data between the clients and the server. The proposed framework initiates the process by constructing local decision trees on each client's side to produce local explanations. Subsequently, by using rule extraction from these trees and strategically sorting and merging those rules, the server obtains a merged set of rules suitable to be used as a global explainer. We empirically evaluate the performance of GLOR-FLEX on three distinct tabular data sets, showing high fidelity scores between the explainers and both the local and global models. Our results support the effectiveness of GLOR-FLEX in generating accurate explanations that efficiently detect and explain the behavior of both local and global models.

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