64 citations · 233 across the 32 of their papers we have counts for
4 papers · 1 filter
Revocable Federated Learning: A Benchmark of Federated Forest
Yang Liu, Zhuo Ma, Ximeng Liu +3
A learning federation is composed of multiple participants who use the federated learning technique to collaboratively train a machine learning model without directly revealing the…
Lightning-Fast and Privacy-Preserving Outsourced Computation in the Cloud
Ximeng Liu, Robert H. Deng, Pengfei Wu +1
In this paper, we propose a framework for lightning-fast privacy-preserving outsourced computation framework in the cloud, which we refer to as LightCom. Using LightCom, a user can…
Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing
Yang Liu, Zhuo Ma, Ximeng Liu +3
Recently, Google and other 24 institutions proposed a series of open challenges towards federated learning (FL), which include application expansion and homomorphic encryption (HE)…
Achieving Trust-Based and Privacy-Preserving Customer Selection in Ubiquitous Computing
Chuan Zhang, Liehuang Zhu, Chang Xu +4
The recent proliferation of smart devices has given rise to ubiquitous computing, an emerging computing paradigm which allows anytime & anywhere computing possible. In such a ubiqu…