22 citations · 31 across the 20 of their papers we have counts for
5 papers · 1 filter
BGTplanner: Maximizing Training Accuracy for Differentially Private Federated Recommenders via Strategic Privacy Budget Allocation
Xianzhi Zhang, Yipeng Zhou, Miao Hu +4
To mitigate the rising concern about privacy leakage, the federated recommender (FR) paradigm emerges, in which decentralized clients co-train the recommendation model without expo…
Federated Continual Graph Learning
Yinlin Zhu, Miao Hu, Di Wu
Managing evolving graph data presents substantial challenges in storage and privacy, and training graph neural networks (GNNs) on such data often leads to catastrophic forgetting,…
PPVF: An Efficient Privacy-Preserving Online Video Fetching Framework with Correlated Differential Privacy
Xianzhi Zhang, Yipeng Zhou, Di Wu +3
Online video streaming has evolved into an integral component of the contemporary Internet landscape. Yet, the disclosure of user requests presents formidable privacy challenges. A…
A Survey on Privacy-Preserving Caching at Network Edge: Classification, Solutions, and Challenges
Xianzhi Zhang, Yipeng Zhou, Di Wu +4
Caching content at the edge network is a popular and effective technique widely deployed to alleviate the burden of network backhaul, shorten service delay and improve service qual…
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning
Yinlin Zhu, Xunkai Li, Zhengyu Wu +3
Subgraph federated learning (subgraph-FL) is a new distributed paradigm that facilitates the collaborative training of graph neural networks (GNNs) by multi-client subgraphs. Unfor…