2 papers
cs.CR2025
Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective
Zhongjian Zhang, Mengmei Zhang, Xiao Wang +4
To preserve user privacy in recommender systems, federated recommendation (FR) based on federated learning (FL) emerges, keeping the personal data on the local client and updating…
cs.LG2023
Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation
Bo Yan, Yang Cao, Haoyu Wang +3
The heterogeneous information network (HIN), which contains rich semantics depicted by meta-paths, has emerged as a potent tool for mitigating data sparsity in recommender systems.…