## 4 Experimental Results
### Results  
This section discusses the results obtained from comparing the two proposed methods, VeriFi, and FedEraser using seven datasets under three deletion strategies to test the efficiency of OFU-Ontology and EG-FedUnlearn; bold values indicate the best result among compared methods. SPARQL B1 in Appendix B shows the SPARQL query used to extract the relevance information from the ontology used for MNIST that represents handwritten digits, with concepts such as \"Digit\", \"Stroke Pattern\", and \"Writing Style\". Relationships like \"is similar to\" (e.g., one is similar to seven) are used to capture common confusions, which is useful for relevance scoring during selective unlearning. This query selects digits that are complex and have similarities to other digits (e.g., \"one\" and \"seven\"), which helps in targeted unlearning without adversely affecting accuracy. Leveraging the relationships between similar digits, unlearning was conducted in a way that maintained high model accuracy. Table 11 and Figure 9 show the performance for the MNIST dataset where OFU-Ontology achieved a maximum overall accuracy of 95% and efficiency toward unlearning of 97% for class-based deletion and therefore was much better than the other methods with residual influence values reaching 0.01 and convergence times of 0.78. EG-FedUnlearn was also competitive (with an accuracy of around 92%), but OFU-Ontology still outperformed it in removing influences with 98% unlearning accuracy given class-based deletion. Slightly more computational cost was incurred using OFU-Ontology compared to VeriFi; however, that cost was justified by performance relevant to the application and communication overhead. The ontology used for CIFAR-10 is an image-based ontology that includes concepts such as \"Object\", \"Category\", \"Color\", \"Texture\", and \"Context\". Relationships such as \"is part of\" (e.g., a wheel is part of a car) and \"has color\" (e.g., sky has the color blue) are used to enhance the relevance scoring during unlearning. SPARQL B2 shows the SPARQL query used to extract the relevance of specific images based on object type and color. This query identifies images that belong to important categories like \"Vehicle\" or \"Animal\" and assigns relevance scores accordingly to decide their unlearning priority. The use of relationships like \"has color\" and \"is part of\" ensured comprehensive relevance scoring, allowing for targeted unlearning with minimized residual influence. Table 10 and Figure 10 show the performance for CIFAR-10 dataset across deletion strategies, where a maximum value of accuracy 0.93 was recorded, with unlearning efficiency recorded to be its highest of 0.95 for OFU-Ontology. The residual influence was brought down to 0.02, better than FedEraser and VeriFi's 0.05-0.07. A slight decrease in communication overhead was observed to occur, in the range of 0.55-0.6, whereas the convergence time was also faster at 0.75 compared to 0.81 for FedEraser. The accuracy of unlearning stood at 0.94, guaranteeing efficient removal of data, and the computational cost was recorded at 0.67, balanced by its global good. EG-FedUnlearn was rather competitive, with accuracy fluctuating between 0.89 and 0.91 and a 0.92 unlearning efficiency. The ontology for the UCI Adult dataset represents demographic information such as \"Age\", \"Occupation\", \"Education\", \"Income\", and \"Marital Status\". Relationships like \"is related to Income\" and \"has Education Level\" help assign relevance scores for selective unlearning. SPARQL B3 shows the SPARQL query that is used to select individuals with  
Figure 10: Performance comparison of federated unlearning algorithms for the CIFAR-10 dataset. Figure 9: Performance comparison of federated unlearning algorithms for the MNIST dataset. specific income and education levels to focus the unlearning process on records that may be deemed sensitive to prioritize the unlearning of individuals based on sensitive attributes such as income level and marital status.