Semi-decentralized Federated Ego Graph Learning for Recommendation

• URL: http://arxiv.org/abs/2302.10900v1
• Date: Fri, 10 Feb 2023 03:57:45 GMT
• Title: Semi-decentralized Federated Ego Graph Learning for Recommendation
• Authors: Liang Qu, Ningzhi Tang, Ruiqi Zheng, Quoc Viet Hung Nguyen, Zi Huang, Yuhui Shi, Hongzhi Yin
• Abstract summary: We propose a semi-decentralized federated ego graph learning framework for on-device recommendations, named SemiDFEGL. The proposed framework is model-agnostic, meaning that it could be seamlessly integrated with existing graph neural network-based recommendation methods and privacy protection techniques.
• Score: 58.21409625065663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Collaborative filtering (CF) based recommender systems are typically trained based on personal interaction data (e.g., clicks and purchases) that could be naturally represented as ego graphs. However, most existing recommendation methods collect these ego graphs from all users to compose a global graph to obtain high-order collaborative information between users and items, and these centralized CF recommendation methods inevitably lead to a high risk of user privacy leakage. Although recently proposed federated recommendation systems can mitigate the privacy problem, they either restrict the on-device local training to an isolated ego graph or rely on an additional third-party server to access other ego graphs resulting in a cumbersome pipeline, which is hard to work in practice. In addition, existing federated recommendation systems require resource-limited devices to maintain the entire embedding tables resulting in high communication costs. In light of this, we propose a semi-decentralized federated ego graph learning framework for on-device recommendations, named SemiDFEGL, which introduces new device-to-device collaborations to improve scalability and reduce communication costs and innovatively utilizes predicted interacted item nodes to connect isolated ego graphs to augment local subgraphs such that the high-order user-item collaborative information could be used in a privacy-preserving manner. Furthermore, the proposed framework is model-agnostic, meaning that it could be seamlessly integrated with existing graph neural network-based recommendation methods and privacy protection techniques. To validate the effectiveness of the proposed SemiDFEGL, extensive experiments are conducted on three public datasets, and the results demonstrate the superiority of the proposed SemiDFEGL compared to other federated recommendation methods.

Related papers

• Efficient and Robust Regularized Federated Recommendation [52.24782464815489]
  The recommender system (RSRS) addresses both user preference and privacy concerns. We propose a novel method that incorporates non-uniform gradient descent to improve communication efficiency. RFRecF's superior robustness compared to diverse baselines.
  arXiv  Detail & Related papers  (2024-11-03T12:10:20Z)
• FedRKG: A Privacy-preserving Federated Recommendation Framework via Knowledge Graph Enhancement [20.214339212091012]
  Federated Learning (FL) has emerged as a promising approach for preserving data privacy in recommendation systems by training models locally. Recent Graph Neural Networks (GNN) have gained popularity in recommendation tasks due to their ability to capture high-order interactions between users and items. We propose FedRKG, a novel federated recommendation system, where a global knowledge graph (KG) is constructed and maintained on the server using publicly available item information.
  arXiv  Detail & Related papers  (2024-01-20T02:38:21Z)
• Preference and Concurrence Aware Bayesian Graph Neural Networks for Recommender Systems [5.465420718331109]
  Graph-based collaborative filtering methods have prevailing performance for recommender systems. We propose an efficient generative model that jointly considers the preferences of users, the concurrence of items and some important graph structure information.
  arXiv  Detail & Related papers  (2023-11-30T11:49:33Z)
• APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation [86.29366168836141]
  We propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR) APGL4SR incorporates adaptive and personalized global collaborative information into sequential recommendation systems. As a generic framework, APGL4SR can outperform other baselines with significant margins.
  arXiv  Detail & Related papers  (2023-11-06T01:33:24Z)
• Vertical Federated Graph Neural Network for Recommender System [1.7802147489386633]
  Graph neural network (GNN) is becoming a promising approach for federated recommender systems. Our study proposes the first vertical federated GNN-based recommender system, called VerFedGNN.
  arXiv  Detail & Related papers  (2023-03-10T08:39:26Z)
• FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings [108.77460689459247]
  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.
  arXiv  Detail & Related papers  (2022-10-25T01:08:20Z)
• Self-supervised Graph-based Point-of-interest Recommendation [66.58064122520747]
  Next Point-of-Interest (POI) recommendation has become a prominent component in location-based e-commerce. We propose a Self-supervised Graph-enhanced POI Recommender (S2GRec) for next POI recommendation. In particular, we devise a novel Graph-enhanced Self-attentive layer to incorporate the collaborative signals from both global transition graph and local trajectory graphs.
  arXiv  Detail & Related papers  (2022-10-22T17:29:34Z)
• Self-Supervised Hypergraph Transformer for Recommender Systems [25.07482350586435]
  Self-Supervised Hypergraph Transformer (SHT) Self-Supervised Hypergraph Transformer (SHT) Cross-view generative self-supervised learning component is proposed for data augmentation over the user-item interaction graph.
  arXiv  Detail & Related papers  (2022-07-28T18:40:30Z)
• Decentralized Collaborative Learning Framework for Next POI Recommendation [39.65626819903099]
  Next Point-of-Interest (POI) recommendation has become an indispensable functionality in Location-based Social Networks (LBSNs) accurate recommendation requires a vast amount of historical check-in data, thus threatening user privacy as the location-sensitive data needs to be handled by cloud servers. We propose a novel decentralized collaborative learning framework for POI recommendation (DCLR), which allows users to train their personalized models locally in a collaborative manner.
  arXiv  Detail & Related papers  (2022-03-30T11:00:11Z)
• Federated Social Recommendation with Graph Neural Network [69.36135187771929]
  We propose fusing social information with user-item interactions to alleviate it, which is the social recommendation problem. We devise a novel framework textbfFedrated textbfSocial recommendation with textbfGraph neural network (FeSoG)
  arXiv  Detail & Related papers  (2021-11-21T09:41:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.