## 2 Literature Review
In principle, these works are effective when the storage of data and computations is centralized, but they have to be adapted to perform on federated systems where nodes with numerous gradients have to be shared. More recent procedures, like [PERSON] et al. [13], performed remote and controlled experiments with model updates on each client's devices and then applied selective erasing of their data from other clients, which rendered the need for complete retraining unnecessary. Notably, these methods encounter difficulties with relevance triage as they do not target specific data points that are important regarding the model's performance or accuracy. Moreover, federated unlearning needs to strike a trade-off among accuracy retention, computational efficiency, and privacy preservation. [PERSON] et al. optimize for low memory and low computational overhead, and their approach is therefore ideal for client-side model adaptation on resource constrained devices often employed in FL [14]. Nonetheless, many of these approaches introduce an accuracy or privacy leakage minimization trade-off for efficiency. As federated learning models get more sophisticated and distributed, it is critical to have data-aware unlearning, especially when we need to guarantee that the removed data are relevant for the model behavior. Ontologies create a standard model for specifying entities and relationships, as well as attributes in context to a specific domain. Using ontologies, data points could be organized according to conceptual relationships rather than statistical features, which would enable model interpretability and the prioritization of more relevant/proper data in the training phase. The ontologies first defined by [PERSON] (1955) as \"an explicit specification of a conceptualization\" provide the foundation for your ontology-based systems. Such systems permit the hierarchical structuring of knowledge and linking concepts so that one can manage intelligent data. In ontology-driven relevance in unlearning contexts, we exploit this structured approach to distinguish data points by their semantic importance ([PERSON] et al., 2014). In healthcare, symptoms might be associated with diagnoses within an ontology, allowing the system to prioritize key symptoms relating to a given diagnosis while de-prioritizing non-relevant information for unlearning. Relevance-based scoring is essential for domains with complex and interrelated data, like in medical diagnostics, where some features can be more relevant to the expected results of the model. Ontologies have been applied to ML models in unique domains like personalized recommendation systems and natural language processing, where the relationships between multiple entities can help increase relevance scoring of data to create stronger models. Ontology-driven relevance can serve as a guide in federated unlearning that enables selective data forgetting concerning the semantic hierarchy and importance of data points ([PERSON] et al., 2014). Focusing on the relevant data points that maximize accuracy helps to reduce excessive information loss and improve the unlearning process. An untapped direction away from the typical federated learning context is toward ontologies, which present a considerable opportunity for finding new areas of model improvement through unlearning that takes context into account. Figure 4: Federated unlearning workflow. While federated unlearning allows for the removal of particular data points from a model to protect privacy, existing methods usually treat all data as being equally important with no regard whatsoever for contextual importance. With ontology-based relevance scoring, federated unlearning focuses on keeping highly relevant data and forgetting less relevant information. Therefore, the model retains accuracy by retaining crucial knowledge after unlearning. Also, it may reduce computational and memory overhead on client devices by focusing unlearning on less valuable or more outdated data--a big plus in resource-constrained federated learning scenarios.