Towards Adaptive Personalized Federated Meta-Learning Recommendation

Federated recommendation systems aim to provide high-quality recommendations while protecting user privacy. However, existing federated recommendation algorithms typically use a unified item embedding framework, where all clients share the same item representations. Such approaches often fail to capture users’ personalized perceptions of the same item and struggle to reflect subtle changes in user preferences, limiting the effectiveness of personalized recommendations. Moreover, statistical heterogeneity among clients poses additional challenges for model optimization. To address these issues, we propose APFMRec, a personalized federated recommendation framework that enhances both personalization and global optimization. APFMRec incorporates an adaptive item embedding module to dynamically adjust item representations based on individual user preferences. In addition, a meta-learning update module is designed to mitigate statistical heterogeneity and improve collaborative optimization across clients. Extensive experiments on five real-world datasets demonstrate the effectiveness of APFMRec. The proposed framework consistently outperforms existing federated recommendation methods, achieving up to 8.1% improvement in HR@10 and 7.8% improvement in NDCG@10.