The implementation of recommender systems serves as a crucial remedy for the issue of excessive information in various domains such as E-commerce, Healthcare sector, social media platforms etc. These systems have the capability to make personalized recommendations that are likely to be of relevance and interest to target users. The dataset used by recommender systems often contains sensitive data of users, thereby giving rise to issues related to security and user’s privacy. Nevertheless, the task of striking a balance between providing precise recommendations and safeguarding user privacy remains a challenge. To deal with these challenges federated learning (FL) is a framework that prioritizes privacy by enabling multiple users to simultaneously train a centralized model without the need to disclose their individual private data. In contrast, the unprocessed data residing on edge devices is utilized for training the model within the local environment, thereby enhancing the protection of data privacy. Block-chain technology in the federated learning framework makes the recommender system more secure and it protects user’s private information. Block-chain is not only popular because of its security and privacy salient features, but also due to its resilience, adaptability, fault tolerance and trust characteristics Block-chain technology’s immutability and decentralization ensure the training process and model updates’ integrity and traceability. Block-chain technology improves the collaborative part of federated learning, boosting trust among users who actively contribute data to the model. This chapter delves into the concept of federated learning as it pertains to its application in recommender systems, and explores how block-chain technology focuses on preserving the privacy of personal user data. This study also examines the challenges encountered during the implementation of the federated learning model, as well as the various privacy concerns within the recommender system field.