26 citations · 33 across the 8 of their papers we have counts for
8 papers
Enhanced Byzantine-Robust Federated Learning Via Truncated-Quadratic Loss for Heterogeneous Data
Zhi-Yong Wang, Hao Nan Sheng, Werner Stefan +3
Federated learning distributes data among clients, making it vulnerable to malicious attacks and data heterogeneity, which together pose challenges for robust learning. To tack…
Memo-SQL: Structured Decomposition and Experience-Driven Self-Correction for Training-Free NL2SQL
Zerui Yang, Weichuan Wang, Yanwei Xu +4
Existing NL2SQL systems face two critical limitations: (1) they rely on in-context learning with only correct examples, overlooking the rich signal in historical error-fix pairs th…
Towards Improving Interpretability of Language Model Generation through a Structured Knowledge Discovery Approach
Shuqi Liu, Han Wu, Guanzhi Deng +3
Knowledge-enhanced text generation aims to enhance the quality of generated text by utilizing internal or external knowledge sources. While language models have demonstrated impres…
Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning
Chih Wei Ling, Chun Hei Michael Shiu, Youqi Wu +4
Training machine learning models on decentralized private data via federated learning (FL) poses two key challenges: communication efficiency and privacy protection. In this work,…
Bi-Chainer: Automated Large Language Models Reasoning with Bidirectional Chaining
Shuqi Liu, Bowei He, Linqi Song
Large Language Models (LLMs) have shown human-like reasoning abilities but still face challenges in solving complex logical problems. Existing unidirectional chaining methods, such…
Layered Randomized Quantization for Communication-Efficient and Privacy-Preserving Distributed Learning
Guangfeng Yan, Tan Li, Tian Lan +2
Next-generation wireless networks, such as edge intelligence and wireless distributed learning, face two critical challenges: communication efficiency and privacy protection. In th…
A Scalable Test Problem Generator for Sequential Transfer Optimization
Xiaoming Xue, Cuie Yang, Liang Feng +3
Sequential transfer optimization (STO), which aims to improve the optimization performance on a task of interest by exploiting the knowledge captured from several previously-solved…
Gap-closing Matters: Perceptual Quality Evaluation and Optimization of Low-Light Image Enhancement
Baoliang Chen, Lingyu Zhu, Hanwei Zhu +3
There is a growing consensus in the research community that the optimization of low-light image enhancement approaches should be guided by the visual quality perceived by end users…