10 citations · 11 across the 5 of their papers we have counts for
5 papers
Bi-level Personalization for Federated Foundation Models: A Task-vector Aggregation Approach
Yiyuan Yang, Guodong Long, Qinghua Lu +2
Federated foundation models represent a new paradigm to jointly fine-tune pre-trained foundation models across clients. It is still a challenge to fine-tune foundation models for a…
Federated Low-Rank Adaptation for Foundation Models: A Survey
Yiyuan Yang, Guodong Long, Qinghua Lu +3
Effectively leveraging private datasets remains a significant challenge in developing foundation models. Federated Learning (FL) has recently emerged as a collaborative framework t…
Federated Adapter on Foundation Models: An Out-Of-Distribution Approach
Yiyuan Yang, Guodong Long, Tianyi Zhou +3
As foundation models gain prominence, Federated Foundation Models (FedFM) have emerged as a privacy-preserving approach to collaboratively fine-tune models in federated learning (F…
DMGNN: Detecting and Mitigating Backdoor Attacks in Graph Neural Networks
Hao Sui, Bing Chen, Jiale Zhang +4
Recent studies have revealed that GNNs are highly susceptible to multiple adversarial attacks. Among these, graph backdoor attacks pose one of the most prominent threats, where att…
Beyond Dataset Watermarking: Model-Level Copyright Protection for Code Summarization Models
Jiale Zhang, Haoxuan Li, Di Wu +3
Code Summarization Model (CSM) has been widely used in code production, such as online and web programming for PHP and Javascript. CSMs are essential tools in code production, enha…