Improving Federated Graph Recommendation with Semantic Guidance
Graph-based recommendation models effectively capture high-order collaborative signals from user--item interaction graphs. Federated learning (FL) enables privacy-preserving training across distributed clients. However, directly aggregating graph representations under FL is challenging: locally learned structural embeddings are not globally aligned under non-IID data distributions, and naive parameter averaging fails to recover cross-client relational structure. Existing federated graph-based approaches primarily rely on structural aggregation, yet overlook the global semantic knowledge encoded in large language models (LLMs). In this work, we propose a semantic--structural federated grap...
| Authors | Thi Minh Chau Nguyen, Hien Trang Nguyen, Duc Anh Nguyen et al. |
|---|---|
| Primary category | cs.IR |
| Published | 13 June 2026 |
| Last updated at source | 4 September 2026 |
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