The rapid advancement of artificial intelligence and the proliferation of online education platforms have made accurate and personalized recommendation services a critical component of modern learning systems. Traditional recommendation models often exhibit low accuracy due to the cold-start problem and the limited availability of user–item interaction data. Existing solutions have introduced knowledge graphs (KGs) to incorporate semantic relations into recommendation models. However, the improvement in accuracy remains limited. Moreover, KG-based methods commonly rely on locally constructed graphs that are incomplete and unable to exploit information distributed across other educational platforms. In this work, we propose PLRF, a federated knowledge-graph-based recommendation framework that enables collaborative learning across multiple platforms while preserving data privacy. To handle data heterogeneity, PLRF introduces a regularization mechanism during local training to align heterogeneous representations. Extensive evaluations on two real-world datasets demonstrate that PLRF achieves improvements in NDCG, Recall, Precision, and F1 score compared with both non-KG and single-source KG approaches, highlighting the potential of federated knowledge graph learning in cross-platform personalized recommendation.