359 citations · 796 across the 15 of their papers we have counts for
15 papers
Extracting Forgotten Prompts from Targeted Unlearned Models
Au Ashley Hoi-Ting, Meghdad Kurmanji, William F. Shen +2
Recent unlearning methods (e.g. NPO, DPO, LUNAR) make use of refusal alignment to suppress forgotten data. However, it has been shown that refusal responses might leave traces of u…
DG-CoLearn: An Efficient Collaborative Learning Framework for Dynamic Graphs
Ashley Hoi-Ting Au, Zikun Zhang, Ligang He +1
Dynamic graph learning (DGL) is essential for modelling evolving graph data, but existing methods suffer from significant computational overhead due to repeated full-snapshot retra…
Lethe: Principled Dual-Stream Update for Persistent Knowledge Erasure in Federated Unlearning
Wentai Wu, Hanwei Tan, Yijun Quan +4
Federated unlearning (FU) aims to erase knowledge from a global model. Existing studies commonly assume that federated collaboration terminates after unlearning, overlooking a depl…
PSNet: Fast Data Structuring for Hierarchical Deep Learning on Point Cloud
Luyang Li, Ligang He, Jinjin Gao +1
In order to retain more feature information of local areas on a point cloud, local grouping and subsampling are the necessary data structuring steps in most hierarchical deep learn…
Self-Supervised Leaf Segmentation under Complex Lighting Conditions
Xufeng Lin, Chang-Tsun Li, Scott Adams +9
As an essential prerequisite task in image-based plant phenotyping, leaf segmentation has garnered increasing attention in recent years. While self-supervised learning is emerging…
Reinforcement Learning for Security-Aware Computation Offloading in Satellite Networks
Saurav Sthapit, Subhash Lakshminarayana, Ligang He +2
The rise of NewSpace provides a platform for small and medium businesses to commercially launch and operate satellites in space. In contrast to traditional satellites, NewSpace pro…
Variation-Incentive Loss Re-weighting for Regression Analysis on Biased Data
Wentai Wu, Ligang He, Weiwei Lin
Both classification and regression tasks are susceptible to the biased distribution of training data. However, existing approaches are focused on the class-imbalanced learning and…
FedProf: Selective Federated Learning with Representation Profiling
Wentai Wu, Ligang He, Weiwei Lin +1
Federated Learning (FL) has shown great potential as a privacy-preserving solution to learning from decentralized data that are only accessible to end devices (i.e., clients). In m…
An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee
Tiansheng Huang, Weiwei Lin, Wentai Wu +3
The issue of potential privacy leakage during centralized AI's model training has drawn intensive concern from the public. A Parallel and Distributed Computing (or PDC) scheme, ter…
Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systems
Wentai Wu, Ligang He, Weiwei Lin +1
Mobile Edge Computing (MEC), which incorporates the Cloud, edge nodes and end devices, has shown great potential in bringing data processing closer to the data sources. Meanwhile,…
SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead
Wentai Wu, Ligang He, Weiwei Lin +3
Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence. However, it is very challen…
Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality
Wentai Wu, Ligang He, Weiwei Lin +4
On-line detection of anomalies in time series is a key technique used in various event-sensitive scenarios such as robotic system monitoring, smart sensor networks and data center…
Optimizing the SSD Burst Buffer by Traffic Detection
Xuanhua Shi, Wei Liu, Ligang He +3
Currently, Burst buffer has been proposed to manage the SSD buffering of bursty write requests. Although burst buffer can improve I/O performance in many cases, we find that it has…
MURS: Mitigating Memory Pressure in Service-oriented Data Processing Systems
Xuanhua Shi, Xiong Zhang, Ligang He +3
Although a data processing system often works as a batch processing system, many enterprises deploy such a system as a service, which we call the service-oriented data processing s…
Lifetime-Based Memory Management for Distributed Data Processing Systems
Lu Lu, Xuanhua Shi, Yongluan Zhou +5
In-memory caching of intermediate data and eager combining of data in shuffle buffers have been shown to be very effective in minimizing the re-computation and I/O cost in distribu…