Concurrent vertical and horizontal federated learning with fuzzy cognitive maps
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Abstract (arXiv)
Data privacy is a major concern in industries such as healthcare or finance. The requirement to safeguard privacy is essential to prevent data breaches and misuse, which can have severe consequences for individuals and organisations. Federated learning is a distributed machine learning approach where multiple participants collaboratively train a model without compromising the privacy of their data. However, a significant challenge arises from the differences in feature spaces among participants, known as non-IID data. This research introduces a novel federated learning framework employing fuzzy cognitive maps, designed to comprehensively address the challenges posed by diverse data distributions and non-identically distributed features in federated settings. The proposal is tested through several experiments using four distinct federation strategies: constant-based, accuracy-based, AUC-based, and precision-based weights. The results demonstrate the effectiveness of the approach in achieving the desired learning outcomes while maintaining privacy and confidentiality standards.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 93 references that are in the map. First posted on arXiv 17 Dec 2024.
Builds on
17 of 93 references.
References · 17
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5 more
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- 2019Federated Forestcited by 49
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Citing papers per year: 2026: 1.
1 paper.