activity
20182026
most citedA Framework for Evaluating Gradient Leakage Attacks in Federated Learning

98 citations · 219 across the 30 of their papers we have counts for

collaborators
Showing 2020Show all

5 papers · 1 filter

cs.LG2020

Promoting High Diversity Ensemble Learning with EnsembleBench

Yanzhao Wu, Ling Liu, Zhongwei Xie +3

Ensemble learning is gaining renewed interests in recent years. This paper presents EnsembleBench, a holistic framework for evaluating and recommending high diversity and high accu…

cs.DC2020

Efficient Orchestration of Host and Remote Shared Memory for Memory Intensive Workloads

Juhyun Bae, Gong Su, Arun Iyengar +2

Since very few contributions to the development of an unified memory orchestration framework for efficient management of both host and remote idle memory have been made, we present…

cs.CR2020★ 2 cited

Understanding Object Detection Through An Adversarial Lens

Ka-Ho Chow, Ling Liu, Mehmet Emre Gursoy +3

Deep neural networks based object detection models have revolutionized computer vision and fueled the development of a wide range of visual recognition applications. However, recen…

cs.LG2020★ 98 cited

A Framework for Evaluating Gradient Leakage Attacks in Federated Learning

Wenqi Wei, Ling Liu, Margaret Loper +4

Federated learning (FL) is an emerging distributed machine learning framework for collaborative model training with a network of clients (edge devices). FL offers default client pr…

cs.LG2020★ 13 cited

TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems

Ka-Ho Chow, Ling Liu, Mehmet Emre Gursoy +3

The rapid growth of real-time huge data capturing has pushed the deep learning and data analytic computing to the edge systems. Real-time object recognition on the edge is one of t…