# MFCDR: A Meta-Learning-Based Model for Federated Personalized Cross-Domain Recommendation
###### Abstract  
Cross-domain recommendation systems frequently require the use of rich source domain information to improve recommendations in the target domain, thereby resolving the data sparsity and cold-start problems, whereas the majority of existing approaches frequently require the centralized storage of user data, which poses a substantial risk of privacy breaches. Compared to traditional recommendation systems with centralized data, federated recommendation systems with multiple clients trained collaboratively have significant privacy benefits in terms of user data. While users' interests are often personalized, meta-learning can be used to learn users' personalized preferences, and personalized preferences can help models make recommendations in cold-start scenarios. We use meta-learning to learn the personalized preferences of cold-start users. Therefore, we offer a unique meta-learning-based federated personalized cross-domain recommendation model that discovers the personalized preferences for cold-start users via a server-side meta-recommendation module. To avoid compromising user privacy, an attention mechanism is used on each client to find transferable features that contribute to knowledge transfer while obtaining embeddings of users and items; each client then uploads the weights to the server. The server accumulates weights and delivers them to clients for update. Compared to traditional recommendation system models, our model can effectively protect user privacy while solving the user cold-start problem, as we use an attention mechanism in the local embedding module to mine the source domain for transferable features that contribute to knowledge transfer. Extensive trials on real-world datasets have demonstrated that our technique effectively guarantees speed while protecting user privacy. meta-learning; federated learning; cross-domain recommendation; attention +
Footnote †: journal: Medical Intelligence and Computer Science, Jiangnan University, Wuxi 214126, China  
Footnote †: journal: Medical Intelligence and Computer Science, Jiangnan University Wuxi 214126, China  
Footnote †: journal: Medical Intelligence and Computer Science, Jiangnan University, Wuxi 214126, China  
research problem of our paper is how to prevent user privacy leakage in a cold-start scenario of a cross-domain recommendation system while achieving personalized recommendations for users. How to prevent user privacy leakage is the focus of our research. We combine federated learning with recommendation systems to protect users' private data while using meta-learning for personalized recommendations, an approach that addresses the problem of privacy breaches in traditional recommendation systems. The novelty of our approach is that we use a meta-network to generate transferable embeddings for cold-start users, rather than building a model on each client. Cold-start users do not have interaction items in the target domain, so they need to make use of interaction items in the source domain, and we use an attention mechanism [1] in the process of obtaining user and item embeddings to help find transferable features that contribute to knowledge transfer. Based on these, we propose a novel meta-learning based federated personalized cross-domain recommendation model called MFCDR to solve the user cold-start problem while protecting the user's private data. The MFCDR's core concept is as follows: (i) The central server is responsible for maintaining a globally shared recommendation model, and the user behavior data are stored on the client machine. We upload a random set of local model weights to the server to ensure that user data does not leave the local domain to effectively protect user privacy. The central server then updates the parameters and returns the updated parameters to the client in order to update the global recommendation model.