## 4 Experimental Results
### Results
The ontology for the Purchase dataset includes concepts such as \"Customer\", \"Product Category\", \"Purchase Frequency\", and \"Spending Pattern\". Relationships like \"has purchased\" and \"is a frequent buyer of\" are used to assign relevance scores for products and customers. SPARQL B4 shows the SPARQL query that used to focus on customers who are frequent buyers of specific product categories, allowing targeted unlearning to identify high-relevance customers based on their spending patterns and product categories. Across the UCI Adult and Purchase datasets (Tables A3 and A4, Figures 11 and 12), OFU-Ontology consistently achieved better end results with the highest model accuracy (0.93 for UCI Adult and 0.94 for Purchase) and unlearning efficiency (0.95 for both datasets). EG-FedUnlearn came in second, with accuracies of 0.90 and 0.92, respectively, whereas VeriFi scored 0.88 on UCI Adult. OFU-Ontology went on to reduce the residual influence to 0.02 against VeriFi's 0.06 and enjoyed lower communication overheads (0.54 for UCI Adult and 0.56 for Purchase) than FedEraser (0.62-0.63). Moreover, OFU-Ontology converged much faster (0.73-0.75), with an unlearning accuracy topping out at 0.97, demonstrating that it successfully eliminated the influence of the data at hand. Although OFU-Ontology is somewhat computationally expensive (0.60-0.64), such a computationally expensive protocol is well warranted, owing to the fact that it outperforms every other protocol on almost every metric. The ontology for MIMIC-III represents medical data, including \"Patient\", \"Diagnosis\", \"Treatment\", and \"Medical History\". Relationships such as \"has Diagnosis\" and \"is associated with\" help prioritize unlearning based on patient conditions. SPARQL B5 shows the SPARQL query that is used to select patients with specific diagnoses (e.g., cardiac or respiratory) for unlearning to prioritize patients for unlearning based on medical conditions and treatments. Ontology-based relevance ensured that patients with sensitive medical conditions were prioritized for unlearning, protecting privacy. For the MIMIC-III, KDD Cup 1999, and Reddit Comments datasets (Tables A5-A7, Figures 13-15), OFU-Ontology was found to be better than any other state-of-the-art method across the various evaluation metrics. Model accuracies of 0.89, 0.92, and 0.94 were attained by OFU-Ontology for MIMIC-III, KDD Cup 1999, and Reddit Comments, respectively, while unlearning efficiency scores were 0.93, 0.95, and 0.96, respectively. Residual influences were reduced to 0.01-0.04, far below those recorded by FedEraser and VeriFi, where the residual  
Figure 11: Performance comparison of federated unlearning algorithms for the UCI Adult dataset. influence lay between 0.04 and 0.05. Communication overhead with OFU-Ontology was always low (0.50-0.64), whereas in the case of FedEraser, it hovered around a much higher 0.6-0.75. Concerning convergence time, however, OFU-Ontology was the fastest with 0.72-0.74, and unlearning accuracy was the highest, fluctuating between 0.95 and 0.98, thereby accomplishing extremely precise data removal. The computational cost of OFU-Ontology, given as 0.61-0.68, is something that can be paid in light of its performance in the accuracy, efficiency, and robustness metrics. Similarly performing EG-FedUnlearn slightly lagged behind in accuracy and residual influence. The ontology for the KDD Cup 1999 dataset includes network activities such as \"User\", \"NetworkEvent\", \"Attack Type\", and \"Severity\". Relationships like \"is anomalous\" and \"is part of the network\" help identify and score the relevance for security-related unlearning. SPARQL B6 shows the SPARQL query that is used to focus on network events with high severity and anomalous characteristics for targeted unlearning to identify and prioritize unlearning of network events that are categorized as anomalies.