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.

翻译：数据隐私是医疗保健或金融等行业的主要关切点。保护隐私的要求对于防止数据泄露和滥用至关重要，这些行为可能对个人和组织造成严重后果。联邦学习是一种分布式机器学习方法，多个参与者在无需共享其数据的情况下协作训练模型，从而保障数据隐私。然而，参与者之间特征空间的差异（即非独立同分布数据）带来了重大挑战。本研究提出了一种采用模糊认知图的新型联邦学习框架，旨在全面应对联邦环境中数据分布多样性和特征非独立同分布所带来的挑战。该方案通过使用四种不同的联邦策略（基于常数、基于准确率、基于AUC和基于精确率的权重）进行了多项实验验证。结果表明，该方法在实现预期学习目标的同时，有效维护了隐私和保密标准。