359 citations · 832 across the 13 of their papers we have counts for
15 papers
Seeing the Needle in the Haystack: Towards Weakly-Supervised Log Instance Anomaly Localization via Counterfactual Perturbation
Yutszyuk Wong, Wentai Wu, Yuen-Ying Yeung +1
Log anomaly detection is a critical task for system operations and security assurance. However, in networked systems at scale, log data are generated at massive scale while instanc…
HaS: Accelerating RAG through Homology-Aware Speculative Retrieval
Peng Peng, Weiwei Lin, Wentai Wu +2
Retrieval-Augmented Generation (RAG) expands the knowledge boundary of large language models (LLMs) at inference by retrieving external documents as context. However, retrieval bec…
Exact Federated Continual Unlearning for Ridge Heads on Frozen Foundation Models
Yijun Quan, Wentai Wu, Giovanni Montana
Foundation models are commonly deployed as frozen feature extractors with a small trainable head to adapt to private, user-generated data in federated settings. The ``right to be f…
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…
Task-Agnostic Federation over Decentralized Data: Research Landscape and Visions
Wentai Wu, Ligang He, Saiqin Long +4
Increasing legislation and regulations on private and proprietary information results in scattered data sources also known as the "data islands". Although Federated Learning-based…
CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns
Shengsheng Lin, Weiwei Lin, Xinyi Hu +3
The stable periodic patterns present in time series data serve as the foundation for conducting long-horizon forecasts. In this paper, we pioneer the exploration of explicitly mode…
SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters
Shengsheng Lin, Weiwei Lin, Wentai Wu +2
This paper introduces SparseTSF, a novel, extremely lightweight model for Long-term Time Series Forecasting (LTSF), designed to address the challenges of modeling complex temporal…
SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting
Shengsheng Lin, Weiwei Lin, Wentai Wu +3
RNN-based methods have faced challenges in the Long-term Time Series Forecasting (LTSF) domain when dealing with excessively long look-back windows and forecast horizons. Consequen…
PETformer: Long-term Time Series Forecasting via Placeholder-enhanced Transformer
Shengsheng Lin, Weiwei Lin, Wentai Wu +2
Recently, the superiority of Transformer for long-term time series forecasting (LTSF) tasks has been challenged, particularly since recent work has shown that simple models can out…
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…