9 citations · 11 across the 5 of their papers we have counts for
5 papers
The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective
Zhen Qin, Daoyuan Chen, Wenhao Zhang +5
The rapid development of large language models (LLMs) has been witnessed in recent years. Based on the powerful LLMs, multi-modal LLMs (MLLMs) extend the modality from text to a br…
A Bargaining-based Approach for Feature Trading in Vertical Federated Learning
Yue Cui, Liuyi Yao, Zitao Li +3
Vertical Federated Learning (VFL) has emerged as a popular machine learning paradigm, enabling model training across the data and the task parties with different features about the…
An Auction-based Marketplace for Model Trading in Federated Learning
Yue Cui, Liuyi Yao, Yaliang Li +3
Federated learning (FL) is increasingly recognized for its efficacy in training models using locally distributed data. However, the proper valuation of shared data in this collabor…
Efficient Personalized Federated Learning via Sparse Model-Adaptation
Daoyuan Chen, Liuyi Yao, Dawei Gao +2
Federated Learning (FL) aims to train machine learning models for multiple clients without sharing their own private data. Due to the heterogeneity of clients' local data distribut…
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks
Zeyu Qin, Liuyi Yao, Daoyuan Chen +3
In this work, besides improving prediction accuracy, we study whether personalization could bring robustness benefits to backdoor attacks. We conduct the first study of backdoor at…