6 papers
Towards Interpretable Federated Learning
Anran Li, Rui Liu, Ming Hu +4
Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data. In order for FL to achieve widespre…
FilterFL: Knowledge Filtering-based Data-Free Backdoor Defense for Federated Learning
Yanxin Yang, Ming Hu, Xiaofei Xie +4
As a distributed machine learning paradigm, Federated Learning (FL) enables large-scale clients to collaboratively train a model without sharing their raw data. However, due to the…
Gradients as an Action: Towards Communication-Efficient Federated Recommender Systems via Adaptive Action Sharing
Zhufeng Lu, Chentao Jia, Ming Hu +2
As a promising privacy-aware collaborative model training paradigm, Federated Learning (FL) is becoming popular in the design of distributed recommender systems. However, Federated…
VulStamp: Vulnerability Assessment using Large Language Model
Hao Shen, Ming Hu, Xiaofei Xie +2
Although modern vulnerability detection tools enable developers to efficiently identify numerous security flaws, indiscriminate remediation efforts often lead to superfluous develo…
An Empirical Study of Vulnerability Detection using Federated Learning
Peiheng Zhou, Ming Hu, Xingrun Quan +6
Although Deep Learning (DL) methods becoming increasingly popular in vulnerability detection, their performance is seriously limited by insufficient training data. This is mainly b…
FedQP: Towards Accurate Federated Learning using Quadratic Programming Guided Mutation
Jiawen Weng, Zeke Xia, Ran Li +2
Due to the advantages of privacy-preserving, Federated Learning (FL) is widely used in distributed machine learning systems. However, existing FL methods suffer from low-inference…