Personalized Federated Recommendation With Knowledge Guidance

• URL: http://arxiv.org/abs/2511.12959v2
• Date: Tue, 18 Nov 2025 13:31:54 GMT
• Title: Personalized Federated Recommendation With Knowledge Guidance
• Authors: Jaehyung Lim, Wonbin Kweon, Woojoo Kim, Junyoung Kim, Dongha Kim, Hwanjo Yu,
• Abstract summary: We propose Federated Recommendation with Knowledge Guidance (FedRKG)<n>FedRKG fuses global knowledge into preserved local embeddings, attaining personalization benefits of dual-knowledge within a single-knowledge memory footprint.<n>Experiments on benchmark datasets demonstrate that FedRKG significantly outperforms state-of-the-art methods.
• Score: 18.117610268256005
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Federated Recommendation (FedRec) has emerged as a key paradigm for building privacy-preserving recommender systems. However, existing FedRec models face a critical dilemma: memory-efficient single-knowledge models suffer from a suboptimal knowledge replacement practice that discards valuable personalization, while high-performance dual-knowledge models are often too memory-intensive for practical on-device deployment. We propose Federated Recommendation with Knowledge Guidance (FedRKG), a model-agnostic framework that resolves this dilemma. The core principle, Knowledge Guidance, avoids full replacement and instead fuses global knowledge into preserved local embeddings, attaining the personalization benefits of dual-knowledge within a single-knowledge memory footprint. Furthermore, we introduce Adaptive Guidance, a fine-grained mechanism that dynamically modulates the intensity of this guidance for each user-item interaction, overcoming the limitations of static fusion methods. Extensive experiments on benchmark datasets demonstrate that FedRKG significantly outperforms state-of-the-art methods, validating the effectiveness of our approach. The code is available at https://github.com/Jaehyung-Lim/fedrkg.

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