Personalized federated recommendation via long-horizon local optimization and regularized knowledge guidance

Federated Recommendation (FedRec) enables privacy-preserving recommendation by training models across user devices without sharing raw interaction data. However, personalized FedRec still faces a trade-off between personalization and resource efficiency. In this paper, we revisit personalization from the perspective of the local optimization horizon. We find that sufficiently long local optimization can form user-specific item structures, but frequent synchronization or full replacement can disrupt them. Based on this observation, we propose FedRKG , a model-agnostic framework that forms personalized item embeddings through Long-Horizon Local Optimization and injects common global knowledge through intermittent Regularized Knowledge Guidance . This design incorporates shared collaborative signals without replacing local embeddings or maintaining dual representations. We further introduce Adaptive Guidance to control the influence of global knowledge at the user–item interaction level. Extensive experiments show that FedRKG consistently outperforms state-of-the-art FedRec baselines while reducing storage and communication costs. Our code is available at https://github.com/Jaehyung-Lim/FedRKG_JIIS26_SCIE .