## 2 Background
### Model Management  
Another critical component of the FLS taxonomy is model management, which refers to the approaches and techniques used to manage the machine learning models within an FL framework. It includes several aspects of model deployment, customization, aggregation, initialization, and updates in FL environments. The following elements are essential in understanding model management within the FLS taxonomy:  
* Global model: In an FL environment, the global model represents the machine learning model trained and updated collectively by all participating devices or nodes. Without consolidating the data, the global model captures the common knowledge derived from the decentralized data sources. Most FL situations have a single global model that all participants work together to enhance. Meanwhile, some global models may be employed in other specialized applications, each tailored to a particular task, set of features, or user group. The central focus of FL is the global model, encapsulating collective intelligence from various data sources while safeguarding data privacy and promoting decentralization. The successful management of the global model is crucial, involving appropriate initialization, secure updates, and precise evaluation. * Local model: The term \"local model\" refers to individual machine learning models maintained and updated by each participating device or node in the network. These local models are trained using local data accessible on each individual device, and the raw data is not shared with a centralized server throughout this process. Each participant may have their own unique local model, which they are responsible for maintaining. During the training, participants do not discuss their models, nor do they exchange raw data or model parameters with one another. As an alternative, each participant may maintain their own local ensemble of models, allowing for a variety of perspectives and levels of competence. The ensemble may include models that use various algorithmic approaches, architectural layouts, or hyperparameter settings. More reliable and accurate results can sometimes be achieved by combining the predictions of different models. Local models are essential to the FL process because they enable individuals to contribute to the collective intelligence without compromising the privacy of their personal information. Effective local model management is crucial for the success of FLSs across various domains and applications. This management must include secure training, customization, and evaluation. * Model aggregation: A key component of model management in the FLS taxonomy is model aggregation, which describes the procedure for combining local model updates from many collaborators to produce a current global model. This procedure is essential to FL because it guarantees the integration of all participants' aggregate knowledge without centralizing their raw data [31]. FLSs use various standard methods for aggregating models. Federated averaging is the most widely used model aggregation technique for FL. After using their own data to train their local model, all participantstransmit the updated model--gradients--to a central server. A new global model is produced by averaging these modifications. Since no raw data is transferred, privacy is guaranteed [32]. This approach's efficiency and simplicity enable quick adjustments to the global model. Another popular FL model aggregation method is secure aggregation. This technique combines model updates while protecting the privacy of individual modifications. It uses cryptographic techniques, such as SMPC, to aggregate data without disclosing the unprocessed changes. It is appropriate for sensitive applications since it offers a high degree of privacy and secrecy. Additionally, it safeguards the integrity of the aggregation process from malevolent attempts. Krum-based aggregation, the third aggregation technique, is designed to stave off Byzantine attacks.