## 2 Background
### Model Management
The following key elements pertain to privacy and security within FLSs:  
* Differential privacy: Differential privacy is a core principle within the field of privacy-preserving data analysis, such as in FL. Differential privacy techniques ensure that the presence or absence of a particular data point does not materially affect the output by adding noise to the computations made on the data [34]. Even when combined with or applied to updates to machine learning models, it safeguards the privacy of individual data pieces. Differential privacy is used in FLSs to protect participant data privacy while enabling group participation in machine learning model training [35]. The following are the fundamental types of differential privacy inside FLSs:
* Local differential privacy (LDP): When using LDP, noise is applied locally to individual data points on the users' devices before transferring the perturbed data to the central server. This ensures that raw data are never transmitted outside users' devices, offering a higher level of privacy but making it more difficult to aggregate the data [35]. * Central differential privacy (CDP): In the CDP technique, noise is added to the aggregated statistics or model parameters in a centralized location. This helps to ensure that no participant's data are made public. It is appropriate for situations in which a reliable central server compiles the updates contributed by participants without disclosing their private data [35]. * Epsilon-differential privacy (\(\epsilon\)-differential privacy): The level of privacy can be quantified using a parameter known as epsilon. A lower value for epsilon indicates a greater degree of discretion and confidentiality. A balance must be struck between personal privacy and practicality. Lower values result in increased privacy but could also lead to a less accurate global model. In FLSs, differential privacy is crucial to guaranteeing that users can provide data for model training without risking their privacy. Ensuring the security of sensitive information while maintaining accuracy in models is a critical component of privacy-preserving machine learning in collaborative settings. Table 3 provides a comparison summary between these three standard differential privacy techniques. * Secure multiparty computation: SMPC is a cryptographic methodology that facilitates collaborative computation of a function by numerous entities while ensuring the privacy of their inputs. Within the realm of FLSs, SMPC assumes a pivotal role in upholding privacy and security. The integration of SMPC inside the FLS taxonomy can be elucidated as follows:
* Privacy-preserving model aggregation: SMPC guarantees participants' ability to safely submit their model updates or gradients to collectively construct a global model while ensuring that no individual party can access the specific contributions made by others. The integration of collective intelligence from multiple participants while maintaining individual privacy is of utmost importance in the FL context. * Collaborative model training: The SMPC technique facilitates the cooperative training of machine learning models, allowing participants to cooperatively compute model parameters without sharing their raw data. Collaborative efforts among participants can be employed to enhance the model's accuracy while maintaining the privacy and confidentiality of their respective datasets. * Differential privacy in aggregation: The combination of SMPC and differential privacy approaches allows for the introduction of noise into aggregated results, hence offering a robust privacy assurance. The utilization of the aggregated model guarantees that the determination of the contribution made by any particular
participant remains computationally infeasible, thus upholding the preservation of individual privacy [35].