Concurrent vertical and horizontal federated learning with fuzzy cognitive maps
This addresses data privacy issues in industries like healthcare or finance, though it appears incremental as it builds on existing federated learning methods.
The paper tackles the challenge of non-IID data in federated learning by introducing a framework using fuzzy cognitive maps, which effectively maintains privacy and achieves learning outcomes across four federation strategies.
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.