58 citations · 107 across the 17 of their papers we have counts for
21 papers
AccEar: Accelerometer Acoustic Eavesdropping with Unconstrained Vocabulary
Pengfei Hu, Hui Zhuang, Panneer Selvam Santhalingamy +4
With the increasing popularity of voice-based applications, acoustic eavesdropping has become a serious threat to users' privacy. While on smartphones the access to microphones nee…
Federated Learning Hyper-Parameter Tuning from a System Perspective
Huanle Zhang, Lei Fu, Mi Zhang +4
Federated learning (FL) is a distributed model training paradigm that preserves clients' data privacy. It has gained tremendous attention from both academia and industry. FL hyper-…
Proof of User Similarity: the Spatial Measurer of Blockchain
Shengling Wang, Lina Shi, Hongwei Shi +3
Although proof of work (PoW) consensus dominates the current blockchain-based systems mostly, it has always been criticized for the uneconomic brute-force calculation. As alternati…
Online Learning for Failure-aware Edge Backup of Service Function Chains with the Minimum Latency
Chen Wang, Qin Hu, Dongxiao Yu +1
Virtual network functions (VNFs) have been widely deployed in mobile edge computing (MEC) to flexibly and efficiently serve end users running resource-intensive applications, which…
SPDL: Blockchain-secured and Privacy-preserving Decentralized Learning
Minghui Xu, Zongrui Zou, Ye Cheng +3
Decentralized learning involves training machine learning models over remote mobile devices, edge servers, or cloud servers while keeping data localized. Even though many studies h…
Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing
Qin Hu, Zhilin Wang, Minghui Xu +1
Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost. However, for the co…