66 citations · 324 across the 32 of their papers we have counts for
32 papers
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…
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…
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…
The Effectiveness of Local Updates for Decentralized Learning under Data Heterogeneity
Tongle Wu, Zhize Li, Ying Sun
We revisit two fundamental decentralized optimization methods, Decentralized Gradient Tracking (DGT) and Decentralized Gradient Descent (DGD), with multiple local updates. We consi…
Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression
Sijin Chen, Zhize Li, Yuejie Chi
We consider the problem of finding second-order stationary points of heterogeneous federated learning (FL). Previous works in FL mostly focus on first-order convergence guarantees,…
Coresets for Vertical Federated Learning: Regularized Linear Regression and -Means Clustering
Lingxiao Huang, Zhize Li, Jialin Sun +1
Vertical federated learning (VFL), where data features are stored in multiple parties distributively, is an important area in machine learning. However, the communication complexit…