71 citations · 175 across the 7 of their papers we have counts for
7 papers
Retrieval-Augmented Mixture of LoRA Experts for Uploadable Machine Learning
Ziyu Zhao, Leilei Gan, Guoyin Wang +5
Low-Rank Adaptation (LoRA) offers an efficient way to fine-tune large language models (LLMs). Its modular and plug-and-play nature allows the integration of various domain-specific…
DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization
Zheqi Lv, Wenqiao Zhang, Shengyu Zhang +8
Device Model Generalization (DMG) is a practical yet under-investigated research topic for on-device machine learning applications. It aims to improve the generalization ability of…
Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI
Jiangchao Yao, Shengyu Zhang, Yang Yao +15
Influenced by the great success of deep learning via cloud computing and the rapid development of edge chips, research in artificial intelligence (AI) has shifted to both of the co…
Federated Graph Learning -- A Position Paper
Huanding Zhang, Tao Shen, Fei Wu +3
Graph neural networks (GNN) have been successful in many fields, and derived various researches and applications in real industries. However, in some privacy sensitive scenarios (l…
The Mathematics Behind Spectral Clustering And The Equivalence To PCA
T Shen
Spectral clustering is a popular algorithm that clusters points using the eigenvalues and eigenvectors of Laplacian matrices derived from the data. For years, spectral clustering h…
Federated Unsupervised Representation Learning
Fengda Zhang, Kun Kuang, Zhaoyang You +6
To leverage enormous unlabeled data on distributed edge devices, we formulate a new problem in federated learning called Federated Unsupervised Representation Learning (FURL) to le…
Federated Mutual Learning
Tao Shen, Jie Zhang, Xinkang Jia +6
Federated learning (FL) enables collaboratively training deep learning models on decentralized data. However, there are three types of heterogeneities in FL setting bringing about…