# Reducing Defense Vulnerabilities in Federated Learning:
## 5 Discussion and Conclusions  
As a result of the experimental evaluations, it has been proven that the performance of our proposed NC-FLD method reaches a high success rate. The superior defense of our neuron-centric approach can be explained through the trajectory of the learning, where neurons with high impact represent critical pathways in the model's learning process and present significantly higher variance under attack conditions. By dynamically focusing on these high-impact neurons, NC-FLD essentially concentrates on the most vulnerable points susceptible to poisoning, providing more sensitive detection than approaches analyzing the entire parameter space.  
Although exposed to different attacks on three different datasets, the proposed defense algorithm generally exhibited minimal differences in model accuracy. The computational overhead varies by classifier choice, with OC-SVM introducing approximately 25% additional processing time compared to standard FedAvg, while k-NN offers a more lightweight alternative with only 10% increased computation but reduced detection accuracy under high-adversarial rates. These trade-offs present deployment options across the security-efficiency spectrum based on specific threat models and resource constraints.  
Despite the promising results, several limitations of the NC-FLD approach warrant discussion. Firstly, the scalability to very large federated systems with thousands of participants remains to be thoroughly validated. While our theoretical analysis suggests computational complexity scales linearly with participant count, practical deployment challenges may emerge in vast, distributed environments. Moreover, the effectiveness on extremely deep neural architectures (e.g., transformers with hundreds of layers) requires further investigation, as the relationship between neuron behavior and poisoning attacks may become more complex in such settings. Our current experiments focused on CNN and ResNet architectures, and the patterns observed might differ in other architectural paradigms.  
In future studies, we will investigate reinforcement learning techniques for adaptive neuron selection that can automatically adjust selection criteria based on observed attack patterns. We also aim to develop a hybrid defense mechanism combining our neuron-centric approach with differential privacy guarantees, providing comprehensive protection against both poisoning and privacy attacks. Additionally, we plan to extend our framework to handle sequential attack scenarios by incorporating temporal analysis of neuron behavior across multiple federated rounds. These advances should further increase the rate of accurate gradient detection and have a positive impact on model success, particularly in non-IID data distributions.  
The aim is to develop an effective defense algorithm against poisoning and new attacks in non-IID data distribution by using other classification algorithms in the classification of gradients.  
Conceptualization, E.S.E.; methodology, E.S.E. and H.E.; software, E.S.E. and H.E.; writing--original draft preparation, E.S.E.; writing--review and editing, H.E., B.U., and G.U.; project administration, I.S. All authors have read and agreed to the published version of the manuscript.