# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 2 Overview of the FRS  
An FRS is a distributed system for providing a personalized product and content recommendations across multiple platforms [24]. It is designed to leverage the collective intelligence of the network of participants, providing a more comprehensive view of the user's preferences and needs [25]. Below, we precisely explain the main entities in FRSs. Figure 2 presents the architectural overview of the FRS.  
* **User**: The user entity in an FRS is an individual identified by his/her unique ID. The user is the primary person interacting with the system and provides information to generate personalized recommendations [26]. This includes user preferences, interests, purchase history, and other data that can be used to suggest relevant items. The user  
Figure 2: Overview of the FRS entities.  
entity is also used to store information related to the user's interactions with the system, such as ratings, reviews, feedback, etc. This information can be used to help improve the accuracy of future recommendations.
* **Item**: The item entity in the FRS refers to the items recommended to the users. This could include products, services, books, movies, music, etc. [27]. The items can come from different sources, such as websites, stores, or catalogs. The recommendation system uses the item entity to suggest items to users based on their preferences and interests. The item entity also contains information about the items, such as the price, description, and other relevant details.
* **Cross-system**: The cross-system entity in an FRS is a type of user profile created by combining user data from multiple systems [28]. This allows for more accurate recommendation algorithms to be developed and deployed across multiple systems [29]. For example, if a system collects user data from multiple different websites and platforms, the cross-system entity can be used to create a unified profile of a user across all of these different sources. This unified profile can then be used to generate more accurate recommendations, such as suggesting a product that a user might like based on his/her preferences across all of the different systems.
* **Recommendation**: A recommendation entity in an FRS allows individual users to access personalized recommendations from multiple sources [30; 31]. This type of system is useful for organizations that have multiple data sources and need to be able to provide personalized recommendations to individual users based on their historical experience [32]. The recommendation entity acts as a central hub that can aggregate data from multiple sources and provide tailored recommendations to individual users. This helps organizations make decisions more quickly and efficiently, allowing them to increase customer satisfaction and loyalty.
* **Cloud server**: A cloud server is an entity in an FRS that stores user information and makes recommendations for other users [33]. The cloud server is responsible for collecting data from multiple sources, processing them, and making recommendations. It also is a hub for other participating entities, such as individual users, content providers, and recommendation systems. By connecting these entities, the federated server can provide more accurate and personalized recommendations to users [34].