Defending against label-flipping attacks in federated learning systems using uniform manifold approximation and projection

Abstract

The user experience can be greatly improved by using learning models that have been trained using data from mobile devices and other internet of things (IoT) devices. Numerous efforts have been made to implement federated learning (FL) algorithms in order to facilitate the success of machine learning models. Researchers have been working on various privacy-preserving methodologies, such as deep neural networks (DNN), support vector machines (SVM), logistic regression, and gradient boosted decision trees, to support a wider range of machine learning models. The capacity for computing and storage has increased over time, emphasizing the growing significance of data mining in engineering. Artificial intelligence and machine learning have recently achieved remarkable progress. We carried out research on data poisoning attacks in the FL system and proposed defence technique using uniform manifold approximation and projection (UMAP). We compare the efficiency by using UMAP, principal component analysis (PCA), Kernel principal component analysis (KPCA) and k-mean clustering algorithm. We make clear in the paper that UMAP performs better than PCA, KPCA and k-mean, and gives excellent performance in detection and mitigating against data-poisoning attacks.