## 4 Experimental Results
### Results
The ontology helps effectively target anomalous events for unlearning while preserving legitimate network activities and ensures that all traces of targeted network anomalies are removed, significantly reducing residual influence. Figure 12: Performance comparison of federated unlearning algorithms for the Purchase dataset. Figure 13: Performance comparison of federated unlearning algorithms for the MIMIC-III dataset. The ontology for Reddit Comments includes concepts like \"User\", \"Comment\", \"Sentiment\", and \"Topic\". Relationships like \"has Sentiment\" and \"belongs to Topic\" are used to determine the relevance of specific comments for unlearning. SPARQL B7 shows the SPARQL query that is used to select comments with negative sentiment in sensitive topics for unlearning to prioritize comments for unlearning based on sentiment and topics deemed sensitive. Ontology-driven relevance allowed the effective unlearning of comments related to sensitive topics, which helped mitigate model bias. Figure 16 presents a radar chart that analyzes the results for the two proposed methods, while Figure 17 offers the analysis of the four federated unlearning algorithms EG-FedUnlearn, OFU-Ontology, FedEraser, and VeriFi-across seven criteria spread over various metrics: model accuracy, unlearning efficiency, residual influence, communication overhead, convergence time, unlearning accuracy, and computational cost. In general, OFU-Ontology is the most competent algorithm since, in terms of the metrics, it has better model accuracy, unlearning efficiency, residual influence, and unlearning accuracy compared to the other competitors. Efficiency is calculated by evaluating the reduction in unlearning time and memory usage relative to accuracy loss after deletion. Through the leverage of ontology-based relevance scoring, OFU-Ontology selectively unlearns data  
Figure 14: Performance comparison of federated unlearning algorithms for the KDD Cup 1999 dataset. Figure 15: Performance comparison of federated unlearning algorithms for the Reddit Comments dataset. most effectively without compromising the overall quality of the model. EG-FedUnLearn has a fair share of the attention, especially in terms of convergence time and unlearning efficiency. It maintains a healthy balance between performance and computational cost, as its way of recalculating local updates after the removal of targeted data makes the unlearning highly efficient. FedEraser is mediocre in its performance; however, the high cost of communication and computation makes it less viable for scalable implementations, especially when frequent unlearning is required. Being the simplest of the four, VeriFi has the poorest results in almost all metrics related to unlearning, as it does not actually feature specialized mechanisms for effective data removal. Its key strengths lie in a lower computational cost and reduced communication overhead, making it suitable for federated learning applications where unlearning is not a primary concern. Figure 18 shows how different text features such as the sentiment score, keyword frequency, or topic coherence affect the accuracy of the model with respect to several federated unlearning approaches on Reddit Comments dataset. It indicates how different facets of text data are influencing the model over the methodological change of unlearning. Figure 19 presents the changes in the model's accuracy during federated training and unlearning. Both EG-FedUnLearn and OFU-Ontology have sharp improvements in accuracy with communication rounds, with OFU-Ontology having the highest final accuracy. This depicts the advantages of selective forgetting relevance for maintaining useful knowledge. However, FedEraser and VeriFi, while adhering to unlearning, are slower or a bit stagnant to improve accuracy due to more aggressive methods or they sim  
Figure 16: Efficiency comparison between EG-FedUnLearn and OFU-Ontology. Figure 17: Efficiency comparison between the four federated unlearning algorithms. context.