33 citations · 64 across the 6 of their papers we have counts for
6 papers
Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clients
Chenrui Wu, Zexi Li, Fangxin Wang +1
Federated learning (FL) is a distributed framework for collaboratively training with privacy guarantees. In real-world scenarios, clients may have Non-IID data (local class imbalan…
No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier
Zexi Li, Xinyi Shang, Rui He +2
Data heterogeneity is an inherent challenge that hinders the performance of federated learning (FL). Recent studies have identified the biased classifiers of local models as the ke…
Delving into the Adversarial Robustness of Federated Learning
Jie Zhang, Bo Li, Chen Chen +4
In Federated Learning (FL), models are as fragile as centrally trained models against adversarial examples. However, the adversarial robustness of federated learning remains largel…
Federated Learning with Label Distribution Skew via Logits Calibration
Jie Zhang, Zhiqi Li, Bo Li +4
Traditional federated optimization methods perform poorly with heterogeneous data (ie, accuracy reduction), especially for highly skewed data. In this paper, we investigate the lab…
Adversarial Examples for Good: Adversarial Examples Guided Imbalanced Learning
Jie Zhang, Lei Zhang, Gang Li +1
Adversarial examples are inputs for machine learning models that have been designed by attackers to cause the model to make mistakes. In this paper, we demonstrate that adversarial…
DENSE: Data-Free One-Shot Federated Learning
Jie Zhang, Chen Chen, Bo Li +5
One-shot Federated Learning (FL) has recently emerged as a promising approach, which allows the central server to learn a model in a single communication round. Despite the low com…