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- Lossless and Privacy-Preserving Graph Convolution Network for Federated . . .
In this paper, we focus on cross-user federated recommendation It is worth noting that in cross-user federated learning, "client" and "user" refer to the same concept, so we will use them interchangeably from here on
- Privacy-preserving Graph Convolution Network for Federated Item . . .
We propose a novel federated recommendation framework that canfully model the high-order connectivityin thedecentralized user-item graphand meanwhileprotect user privacywell
- Privacy-preserving graph convolution network for federated item . . .
To address that, we propose a GNN-based federated recommendation framework, i e , privacy-preserving graph convolution network (P-GCN), for the studied problem of FIR Our P-GCN can leverage the high-order connectivity information like a centralized GCN model such as LightGCN, though it is built using a decentralized user-item graph
- This is the code for our research paper: Lossless and Privacy . . .
This is the code for our research paper: Lossless and Privacy-Preserving Graph Convolution Network for Federated Item Recommendation - SZU-GW LP-GCN
- Lossless and Privacy-Preserving Graph Convolution Network for Federated . . .
Lossless and Privacy-Preserving Graph Convolution Network for Federated Item Recommendation
- FedRKG: A Privacy-Preserving Federated Recommendation Framework via . . .
To maximize the utilization of diverse data types while ensuring privacy protection on edge devices, we propose FedRKG 1, a GNN-based federated learning recommendation framework
- Privacy-preserving graph convolution network for federated item . . .
Article "Privacy-preserving graph convolution network for federated item recommendation" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as "JST")
- FedGNN: Federated Graph Neural Network for Privacy-Preserving . . .
In this paper, we propose a federated framework named FedGNN for privacy-preserving GNN-based recommenda-tion, which can effectively exploit high-order user-item in-teraction information by collaboratively training GNN mod-els for recommendation in a privacy-preserving way
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