切尔西球迷你好，我是程烨（CHENG YE）。目前是京都大学情报学研究科的一年级硕士研究生， 在Data Engineering and Platform Research Group从事研究，导师为首藤一幸教授。 本科毕业于关西大学，获工学学士学位，本科导师为 Adachi Naotoshi 副教授。
我的研究兴趣主要包括联邦学习、区块链和 AI 安全。 业余时间喜欢攀岩、足球和健身。
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京都大学2026年4月 - 2028年3月情报学研究科
硕士研究生 -
关西大学2021年4月 - 2026年3月工学学士：环境都市工学部 都市系统工学科
工作经历
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乐天集团2026年8月 - 2026年10月应用工程师
语言
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中文母语 -
日语商务水平 -
英语日常会话
动态
代表性论文 (查看全部 )
GOFA: Gradient-Oriented Backdoor Attack in Vertical Federated Learning
Ye CHENG, Adachi Naotoshi
International Conference on Electrial Computer and Energy Technologies 2026 ICECET2026
Vertical federated learning (VFL) enables multiple organizations with disjoint feature spaces and overlapping sample identities to collaboratively train machine learning models without sharing raw local data. Despite this privacy-preserving paradigm, VFL remains vulnerable to backdoor attacks. In particular, a malicious passive party can inject carefully crafted triggers into local inputs or intermediate embeddings, causing targeted mispredictions during inference. Existing VFL backdoor attacks (e.g., BadVFL) typically assume that the malicious client has additional knowledge of task labels, which conflicts with the core privacy assumptions of VFL. In this paper, we propose GOFA, a gradient-oriented backdoor attack for VFL. GOFA leverages server-provided gradient feedback to construct a poisoned dataset and applies adversarial-example techniques (e.g., FGSM) to mask original features and strengthen trigger learning. Experiments on CIFAR-10 and UCI-HAR demonstrate the effectiveness of our method across multiple settings.
GOFA: Gradient-Oriented Backdoor Attack in Vertical Federated Learning
Ye CHENG, Adachi Naotoshi
International Conference on Electrial Computer and Energy Technologies 2026 ICECET2026
Vertical federated learning (VFL) enables multiple organizations with disjoint feature spaces and overlapping sample identities to collaboratively train machine learning models without sharing raw local data. Despite this privacy-preserving paradigm, VFL remains vulnerable to backdoor attacks. In particular, a malicious passive party can inject carefully crafted triggers into local inputs or intermediate embeddings, causing targeted mispredictions during inference. Existing VFL backdoor attacks (e.g., BadVFL) typically assume that the malicious client has additional knowledge of task labels, which conflicts with the core privacy assumptions of VFL. In this paper, we propose GOFA, a gradient-oriented backdoor attack for VFL. GOFA leverages server-provided gradient feedback to construct a poisoned dataset and applies adversarial-example techniques (e.g., FGSM) to mask original features and strengthen trigger learning. Experiments on CIFAR-10 and UCI-HAR demonstrate the effectiveness of our method across multiple settings.