## 2 Background
### Model Management
This aggregation approach entails sorting the updates from participants according to their impact. The update with the smallest cumulative distance to the k-nearest updates is selected for aggregation [33]. Because it is robust against updates that differ significantly from one another, it can be used in adversarial environments. Trimmed mean aggregation, a popular variation of federated averaging, removes a predetermined proportion of extreme updates before averaging. After sorting the participant updates, the updates with the largest variances from the mean are eliminated. By using this method, the aggregation process becomes more resilient to updates that contain outliers. Participants in the weighted aggregation technique are given varying weights according to the caliber or applicability of their updates. During aggregation, higher weights are assigned to participants who provide more accurate or diverse updates, increasing their contributions' effect on the global model. This allows for the prioritization of more trustworthy or pertinent updates, enhancing the global model's overall quality. In FL, model aggregation is a crucial stage since it establishes the quality and efficacy of the final global model. The best aggregation technique is determined by specific application needs, such as privacy concerns, resilience against adversarial attacks, communication limitations, and required model quality. Effective model aggregation approaches enable FLSs to create precise, reliable, and privacy-preserving global models. * Model updates contain to modifications made to machine learning models during the FL process. Implementing these updates is paramount in improving the models' overall performance, accuracy, and generalization capabilities. On the one hand, local model updates can be employed in scenarios where players train their own local models using their respective datasets, resulting in model updates derived from their individual training procedures. Local updates are computed via methodologies such as stochastic gradient descent (SGD) or its variations, such as federated averaging. The updates are contingent upon the data on individual participants' devices, enabling models to catch localized patterns. On the other hand, in global model updates, changes are computed by aggregating information from the local models of all participants. Global updates are produced by combining the local model updates contributed by several participants. These updates indicate the cumulative understanding of the FL network as a whole. Model updates play a vital role in the FL context, as they enable the integration of the collective intelligence derived from various data sources into one cohesive and improved model. The success of FLSs in many domains and applications heavily relies on the efficient administration of model updates, encompassing privacy protection, security, and adaptability. * Model deployment: Model deployment in FLSs includes the steps required to make the trained machine learning model accessible and functional for generating predictions or providing services to end-users or applications. However, model deployment in FL exhibits notable differences from conventional machine learning model deployment, mostly stemming from the decentralized and privacy-preserving characteristics inherent in the FL methodology. The strategic process of deploying models in FLSs involves striking a compromise between real-time adaptation and safeguarding user privacy and data security. FL involves the collaborative training of models on dispersed devices while preserving the confidentiality of sensitive data within local servers [26]. Following the completion of training, models can be deployed in both online and offline environments. The process of online deployment in FLSs entails the seamless and immediate incorporation of model changes originating from distributed devices. This facilitates the prompt reaction to evolving data patterns and user behaviors in real time.