3 papers
cs.CV2026
One Modality to Forget Them All: Enhancing Cross-Modal Unlearning in Vision-Language Models
Sudharshan Balaji, Yili Ren, Guangjing Wang +2
Machine unlearning is widely used to remove hazardous knowledge from large language models. Modern Vision-Language Models (VLMs), however, process both text and visual inputs, rais…
cs.CR2025
Buffer is All You Need: Defending Federated Learning against Backdoor Attacks under Non-iids via Buffering
Xingyu Lyu, Ning Wang, Yang Xiao +4
Federated Learning (FL) is a popular paradigm enabling clients to jointly train a global model without sharing raw data. However, FL is known to be vulnerable towards backdoor atta…
cs.LG2025
Two Heads Are Better than One: Model-Weight and Latent-Space Analysis for Federated Learning on Non-iid Data against Poisoning Attacks
Xingyu Lyu, Ning Wang, Yang Xiao +4
Federated Learning is a popular paradigm that enables remote clients to jointly train a global model without sharing their raw data. However, FL has been shown to be vulnerable tow…