33 citations · 82 across the 10 of their papers we have counts for
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cs.LG2023★ 4 cited
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…
cs.LG2022★ 24 cited
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…
cs.LG2021★ 33 cited
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…