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

• URL: http://arxiv.org/abs/2210.13686v1
• Date: Tue, 25 Oct 2022 01:08:20 GMT
• Title: FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings
• Authors: Junyi Li, Heng Huang
• Abstract summary: We propose a Federated Graph Recommender System (FedGRec) to mitigate privacy concerns. 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. We perform extensive empirical evaluations to verify the efficacy of using latent embeddings as a proxy of missing interaction graph.
• Score: 108.77460689459247
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 \href{https://federated-learning.org/fl-neurips-2022/}{the FL-NeurIPS'22 workshop}.

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