24 citations · 63 across the 10 of their papers we have counts for
13 papers · 1 filter
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…
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…
3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation
Peter Richtárik, Igor Sokolov, Ilyas Fatkhullin +3
We propose and study a new class of gradient communication mechanisms for communication-efficient training -- three point compressors (3PC) -- as well as efficient distributed nonc…
FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning
Haoyu Zhao, Zhize Li, Peter Richtárik
Federated Averaging (FedAvg, also known as Local-SGD) (McMahan et al., 2017) is a classical federated learning algorithm in which clients run multiple local SGD steps before commun…