3 papers
cs.LG2026
Federated Active Learning Under Extreme Non-IID and Global Class Imbalance
Chen-Chen Zong, Sheng-Jun Huang
Federated active learning (FAL) seeks to reduce annotation cost under privacy constraints, yet its effectiveness degrades in realistic settings with severe global class imbalance a…
cs.LG2025
FLAIN: Mitigating Backdoor Attacks in Federated Learning via Flipping Weight Updates of Low-Activation Input Neurons
Binbin Ding, Penghui Yang, Sheng-Jun Huang
Federated learning (FL) enables multiple clients to collaboratively train machine learning models under the coordination of a central server, while maintaining privacy. However, th…
cs.CV2025
Dual-Head Knowledge Distillation: Enhancing Logits Utilization with an Auxiliary Head
Penghui Yang, Chen-Chen Zong, Sheng-Jun Huang +2
Traditional knowledge distillation focuses on aligning the student's predicted probabilities with both ground-truth labels and the teacher's predicted probabilities. However, the t…