Search Results for author: Tianyuan Zou

Found 4 papers, 1 papers with code

VFLAIR: A Research Library and Benchmark for Vertical Federated Learning

1 code implementation15 Oct 2023 Tianyuan Zou, Zixuan Gu, Yu He, Hideaki Takahashi, Yang Liu, Guangnan Ye, Ya-Qin Zhang

Vertical Federated Learning (VFL) has emerged as a collaborative training paradigm that allows participants with different features of the same group of users to accomplish cooperative training without exposing their raw data or model parameters.

Vertical Federated Learning

Mutual Information Regularization for Vertical Federated Learning

no code implementations1 Jan 2023 Tianyuan Zou, Yang Liu, Ya-Qin Zhang

However, previous works show that parties without labels (passive parties) in VFL can infer the sensitive label information owned by the party with labels (active party) or execute backdoor attacks to VFL.

Vertical Federated Learning

Vertical Federated Learning: Concepts, Advances and Challenges

no code implementations23 Nov 2022 Yang Liu, Yan Kang, Tianyuan Zou, Yanhong Pu, Yuanqin He, Xiaozhou Ye, Ye Ouyang, Ya-Qin Zhang, Qiang Yang

Motivated by the rapid growth in VFL research and real-world applications, we provide a comprehensive review of the concept and algorithms of VFL, as well as current advances and challenges in various aspects, including effectiveness, efficiency, and privacy.

Fairness Privacy Preserving +1

Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learning

no code implementations10 Dec 2021 Yang Liu, Tianyuan Zou, Yan Kang, Wenhan Liu, Yuanqin He, Zhihao Yi, Qiang Yang

An immediate defense strategy is to protect sample-level messages communicated with Homomorphic Encryption (HE), and in this way only the batch-averaged local gradients are exposed to each party (termed black-boxed VFL).

Inference Attack Vertical Federated Learning

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