10 citations · 14 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates
Laizhong Cui, Xiaoxin Su, Yipeng Zhou +1
Federated Learning (FL) is an emerging decentralized learning framework through which multiple clients can collaboratively train a learning model. However, a major obstacle that im…
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…
Optimizing Video Caching at the Edge: A Hybrid Multi-Point Process Approach
Xianzhi Zhang, Yipeng Zhou, Di Wu +4
It is always a challenging problem to deliver a huge volume of videos over the Internet. To meet the high bandwidth and stringent playback demand, one feasible solution is to cache…
Gain without Pain: Offsetting DP-injected Nosies Stealthily in Cross-device Federated Learning
Wenzhuo Yang, Yipeng Zhou, Maio Hu +4
Federated Learning (FL) is an emerging paradigm through which decentralized devices can collaboratively train a common model. However, a serious concern is the leakage of privacy f…
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…