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cs.LG2025
From Risk to Resilience: Towards Assessing and Mitigating the Risk of Data Reconstruction Attacks in Federated Learning
Xiangrui Xu, Zhize Li, Yufei Han +3
Data Reconstruction Attacks (DRA) pose a significant threat to Federated Learning (FL) systems by enabling adversaries to infer sensitive training data from local clients. Despite…
cs.LG2025
Coresets for Clustering Under Stochastic Noise
Lingxiao Huang, Zhize Li, Nisheeth K. Vishnoi +2
We study the problem of constructing coresets for -clustering when the input dataset is corrupted by stochastic noise drawn from a known distribution. In this setting, eval…
cs.LG2025
X-VFL: A New Vertical Federated Learning Framework with Cross Completion and Decision Subspace Alignment
Qinghua Yao, Xiangrui Xu, Zhize Li
Vertical Federated Learning (VFL) enables collaborative learning by integrating disjoint feature subsets from multiple clients/parties. However, VFL typically faces two key challen…