Hideaki Takahashi — accepted AI-conference papers

2024

VFLAIR: A Research Library and Benchmark for Vertical Federated Learning

Tianyuan Zou, Zixuan GU, Yu He, Hideaki Takahashi, Yang Liu, Ya-Qin Zhang

ICLR 2024poster

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. VFL has gained significant attention for its research…

Cited by 8 Source ↗PDF ↗code ↗Scholar ↗

2023

Breaching FedMD: Image Recovery via Paired-Logits Inversion Attack

Hideaki Takahashi, Jingjing Liu, Yang Liu

CVPR 2023poster

Federated Learning with Model Distillation (FedMD) is a nascent collaborative learning paradigm, where only output logits of public datasets are transmitted as distilled knowledge, instead of passing on private model parameters that are susceptible to gradient inversion attacks, a known privacy risk…

Cited by 16 Source ↗PDF ↗code ↗Scholar ↗