# Reducing Defense Vulnerabilities in Federated Learning:
###### Abstract  
Federated learning is a distributed machine learning approach where end users train local models with their own data and combine model updates on a reliable server to create a global model. Despite its advantages, this distributed structure is vulnerable to attacks as end users keep their data and training process private. Current defense mechanisms often fail when facing different attack types or high percentages of malicious participants. This paper proposes a new defense algorithm called Neuron-Centric Federated Learning Defense (NC-FLD), a novel approach that dynamically identifies and analyzes the most significant neurons across model layers rather than examining entire gradient spaces. Unlike existing methods that analyze all parameters equally, NC-FLD creates feature vectors from specifically selected neurons that show the highest training impact, then applies dimensionality reduction to enhance their discriminative features. We conduct experiments with various attack scenarios and different malicious participant rates across multiple datasets (CIFAR-10, F-MNIST, and MNIST). Additionally, we perform simulations on the GTSR dataset as a real-world application. Experimental results demonstrate that NC-FLD successfully defends against diverse attack scenarios in both IID and non-IID dataset distributions, maintaining accuracy above 70% with 40% malicious participation, a 5-15% improvement over the state-of-the-art method, showing enhanced robustness across diverse data distributions while effectively mitigating the impacts of both data and model poisoning attacks. federated learning; deep learning security; poisoning attacks; data poisoning; model poisoning +
Footnote †: journal: Applied sciences  
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system. It also increases scalability and accuracy by distributing the learning load across end-user devices [2]. Despite these remarkable advances, the distributed nature of FL is prone to significant security issues such as privacy protection and data security. The types of attacks that are carried out by exploiting these vulnerabilities and are most emphasized in the literature are poisoning and communication bottleneck attacks [3]. In this study, we consider a scenario of a distributed network that is subject to \"poisoning attacks\", which is the most common in the literature, and on which defense algorithms have been developed. Figure 1 shows poisoning attacks and their types. The figure illustrates the taxonomy of poisoning attacks in FL environments. It presents a hierarchical breakdown of two key aspects: attack methods and attack goals. Under attack methods, the diagram identifies two main categories: data poisoning and model poisoning. The attack goals are similarly divided into two types: target attacks, which are associated with backdoor mechanisms, and untargeted attacks, which are related to Byzantine approaches. This classification helps organize and understand the different strategies malicious actors might employ to compromise federated learning systems. The related works under these categories are discussed further in Section 2. This paper introduces an NC-FLD that fundamentally differs from existing approaches by dynamically identifying and analyzing only the most significant neurons across model layers rather than examining entire gradient spaces. Unlike conventional methods that treat all parameters equally, our approach selectively focuses on neurons exhibiting the highest training impact, creating targeted feature vectors that more effectively capture attack signatures while reducing computational overhead. This neuron-centric perspective represents a paradigm shift from traditional defenses, enabling robust performance even with high malicious participation rates and under non-IID data distributions that typically challenge existing systems.