4 papers
BDPFL: Backdoor Defense for Personalized Federated Learning via Explainable Distillation
Chengcheng Zhu, Jiale Zhang, Di Wu +1
Federated learning is a distributed learning paradigm that facilitates the collaborative training of a global model across multiple clients while preserving the privacy of local da…
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…
A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research
Sicong Cao, Xiaobing Sun, Ratnadira Widyasari +8
The remarkable achievements of Artificial Intelligence (AI) algorithms, particularly in Machine Learning (ML) and Deep Learning (DL), have fueled their extensive deployment across…
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…