3 papers
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…
stat.AP2023
Understanding Heterogeneity of Automated Vehicles and Its Traffic-level Impact: A Stochastic Behavioral Perspective
Xinzhi Zhong, Yang Zhou, Soyoung Ahn +1
This paper develops a stochastic and unifying framework to examine variability in car-following (CF) dynamics of commercial automated vehicles (AVs) and its direct relation to traf…