22 citations · 35 across the 8 of their papers we have counts for
8 papers
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…
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…
cRVR: A Stackelberg Game Approach for Joint Privacy-Aware Video Requesting and Edge Caching
Xianzhi Zhang, Linchang Xiao, Yipeng Zhou +4
As users conveniently stream their favorite online videos, video request records are automatically stored by video content providers, which have a high chance of privacy leakage. U…
FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment
Jiahao Liu, Jiang Wu, Jinyu Chen +3
Different from conventional federated learning, personalized federated learning (PFL) is able to train a customized model for each individual client according to its unique require…
Optimizing the Numbers of Queries and Replies in Federated Learning with Differential Privacy
Yipeng Zhou, Xuezheng Liu, Yao Fu +3
Federated learning (FL) empowers distributed clients to collaboratively train a shared machine learning model through exchanging parameter information. Despite the fact that FL can…
Virtual Reality: A Survey of Enabling Technologies and its Applications in IoT
Miao Hu, Xianzhuo Luo, Jiawen Chen +3
Virtual Reality (VR) has shown great potential to revolutionize the market by providing users immersive experiences with freedom of movement. Compared to traditional video streamin…
On the Practicality of Differential Privacy in Federated Learning by Tuning Iteration Times
Yao Fu, Yipeng Zhou, Di Wu +3
In spite that Federated Learning (FL) is well known for its privacy protection when training machine learning models among distributed clients collaboratively, recent studies have…