mlsanityData to 7 Oct 2026

FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings

arXiv 2210.13686 ↗First posted 25 Oct 2022cs.LGcited by 4

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Abstract (arXiv)

Recommender systems are widely used in industry to improve user experience. Despite great success, they have recently been criticized for collecting private user data. Federated Learning (FL) is a new paradigm for learning on distributed data without direct data sharing. Therefore, Federated Recommender (FedRec) systems are proposed to mitigate privacy concerns to non-distributed recommender systems. However, FedRec systems have a performance gap to its non-distributed counterpart. The main reason is that local clients have an incomplete user-item interaction graph, thus FedRec systems cannot utilize indirect user-item interactions well. In this paper, we propose the Federated Graph Recommender System (FedGRec) to mitigate this gap. Our FedGRec system can effectively exploit the indirect user-item interactions. More precisely, in our system, users and the server explicitly store latent embeddings for users and items, where the latent embeddings summarize different orders of indirect user-item interactions and are used as a proxy of missing interaction graph during local training. We perform extensive empirical evaluations to verify the efficacy of using latent embeddings as a proxy of missing interaction graph; the experimental results show superior performance of our system compared to various baselines. A short version of the paper is presented in hrefhttps://federated-learning.org/fl-neurips-2022/the FL-NeurIPS'22 workshop.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 40 of its 64 references that are in the map. First posted on arXiv 25 Oct 2022.

Builds on

40 of 64 references.

References · 40

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28 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 2.

4 papers.

  1. 2022FeDXL: Provable Federated Learning for Deep X-Risk Optimizationcited by 10
  2. 2023GNN4FR: A Lossless GNN-based Federated Recommendation Frameworkcited by 0
  3. 2026Communication-Efficient Federated AUC Maximization with Cyclic Client Participationcited by 0
  4. 2026Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networkscited by 0