## 1 Introduction
It assigns semantic relevance scores \(R\left(d_{ij}\right)\) to data points using structured knowledge representations (e.g., symptom-diagnosis relationships in healthcare). Low-relevance data is prioritized for unlearning via weighted gradient negation, preserving high-impact knowledge and reducing accuracy degradation. Figure 3 illustrates this motivation, comparing classic federated unlearning with our ontology-based approach that uses semantic relevance scoring to reduce performance degradation. The figure compares classical federated unlearning with uniform data treatment to ontology-based unlearning, where semantic relevance scoring is applied for unlearning of data. This ontology module allows selective unlearning with a label on the least-relevant data points so as to decrease performance degradation in support of intelligent decision-making in privacy-sensitive federated systems. The proposed work delivers three key contributions:  
1. Proposing EG-FedUnlearn, a fast and storage-efficient unlearning method that removes a target client's influence from the federated model without requiring full model retraining. We also propose OFU-Ontology, the first ontology-enhanced federated unlearning approach, which utilizes domain ontologies to guide the unlearning process and protect the remaining useful model knowledge. 2. Providing an analysis of the proposed methods to demonstrate that the target client's data contributions are eliminated from the model. We prove that EG-FedUnlearn achieves the same effect as excluding the client's data from training (equivalent to retraining) under certain assumptions, thus guaranteeing exact unlearning. We also  
Figure 3: Ontology-enhanced federated unlearning motivation. discuss how the ontology in OFU-Ontology helps avoid unintended forgetting of unrelated knowledge. We also analyze security and privacy implications to show that our approaches do not introduce any new privacy leaks and that these techniques can be used in conjunction with verifiability techniques to assure clients of successful unlearning. 3. Performing experiments on benchmark FL datasets to assess methods such as EG-FedUnlearn and OFU-Ontology. The results indicate that our techniques substantially enhance the performance of unlearning, standing far apart from FedEraser and VeriFi. In terms of model unlearning, our approach takes less time (approximately one-tenth the computation and communication overhead of the baselines) and better retains the model (higher remaining accuracy on test data after unlearning). We demonstrate that OFU-Ontology maintains model utility especially well--the global model suffers a much smaller accuracy drop on non-forgotten data, thanks to ontology-guided knowledge preservation. These improvements illustrate the practical benefit of our contributions to enable efficient, reliable, and accurate federated unlearning in real-world scenarios. By addressing efficiency, completeness, and knowledge-retention challenges with our two approaches, this work paves the way for federated learning systems that can forget users on demand with minimal performance penalty. To the best of our knowledge, this is the first work to integrate ontological domain knowledge into the federated unlearning process, opening a new direction for research in safe and intelligent model unlearning. We also explain how the ontology is constructed and used in our approach. For our experiments, domain ontologies are designed manually based on expert knowledge and public schema definitions, making them interpretable and adaptable across datasets. While this work uses static ontologies for clarity and evaluation, the framework supports future extension to dynamic or automatically generated ontologies that evolve with new data or client feedback. We also include ablation experiments comparing OFU-Ontology with and without the ontology-based relevance scoring, showing that the ontology component significantly improves model utility after unlearning.