6 papers
GuardFed: A Trustworthy Federated Learning Framework Against Dual-Facet Attacks
Yanli Li, Yanan Zhou, Zhongliang Guo +6
Federated learning (FL) enables privacy-preserving collaborative model training but remains vulnerable to adversarial behaviors that compromise model utility or fairness across sen…
CAKD: A Correlation-Aware Knowledge Distillation Framework Based on Decoupling Kullback-Leibler Divergence
Zao Zhang, Huaming Chen, Pei Ning +2
In knowledge distillation, a primary focus has been on transforming and balancing multiple distillation components. In this work, we emphasize the importance of thoroughly examinin…
Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges
Yanli Li, Zhongliang Guo, Nan Yang +3
Federated Learning (FL) offers innovative solutions for privacy-preserving collaborative machine learning (ML). Despite its promising potential, FL is vulnerable to various attacks…
Fairpriori: Improving Biased Subgroup Discovery for Deep Neural Network Fairness
Kacy Zhou, Jiawen Wen, Nan Yang +3
While deep learning has become a core functional module of most software systems, concerns regarding the fairness of ML predictions have emerged as a significant issue that affects…
On Security Weaknesses and Vulnerabilities in Deep Learning Systems
Zhongzheng Lai, Huaming Chen, Ruoxi Sun +3
The security guarantee of AI-enabled software systems (particularly using deep learning techniques as a functional core) is pivotal against the adversarial attacks exploiting softw…
Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics for Federated Learning
Yanli Li, Jehad Ibrahim, Huaming Chen +2
A large number of federated learning (FL) algorithms have been proposed for different applications and from varying perspectives. However, the evaluation of such approaches often r…