Jul 2026· 2026 6th International Conference on Electrical, Computer and Energy Technologies (ICECET)· pp. 1-6· 0 citations· 5 references
Abstract
Vertical Federated Learning (VFL) allows multiple organizations with disjoint feature spaces and overlapping sample identities to collaboratively train machine learning models while keeping local data. Despite its privacy-preserving design, recent studies have shown that VFL is vulnerable to backdoor attacks. In particular, a malicious passive party can inject a carefully crafted trigger into its local inputs or intermediate embeddings, causing the global model to produce targeted mispredictions at inference time. Existing backdoor attacks in VFL (e.g., BadVFL) rely on the assumption that the malicious client can obtain additional knowledge about task labels, which contradicts the core privacy assumptions of VFL. In this paper, we propose GOFA, a new backdoor attack method for VFL scenarios. GOFA uses the gradient feedback from the server to construct a poisoned dataset and employs an adversarial example attack (e.g., FGSM) for masking the original features of the data to enhance trigger learning. We use the CIFAR-10 and UCI-HAR datasets. Our experiments demonstrate the effectiveness of our method in various settings.
This work proposes CAEBA (Conditional AutoEncoder Backdoor Attack), a dynamic hidden backdoor framework that uses a conditional autoencoder to generate target-aware and visually stealthy triggers while progressively implanting the backdoor through federated optimization.
Federated Learning (FL) has emerged as a leading paradigm for privacy-preserving machine learning, yet the distributed nature of FL introduces unique security challenges, notably the threat of backdoor attacks. However, existing attack strategies face a critical limitation: reliance on end-to-end supervision creates a...
Chengcheng Zhu, Ye Li, Bosen Rao et al.· IEEE Transactions on Informa...· 0 citations
The rapid adoption of Internet of Things (IoT) devices has accelerated the need for privacy-preserving machine learning techniques, such as federated learning (FL). However, the decentralized and collaborative nature of FL makes it vulnerable to backdoor attacks, where adversaries locally update their malicious models...
Hal Ferguson, Rui Ning, Hongyi Wu et al.· IEEE Internet of Things Jour...· 0 citations
This work reveals that backdoor behaviors tend to be absorbed by a simpler parallel branch when jointly trained with the main network, and proposes Trapping and Removing (TR), a simple yet effective training-time defense that introduces a lightweight shortcut branch as a "honeypot" to trap backdoor knowledge.
Zixuan Zhu, Rui Wang, Lihua Jing et al.· Proceedings of the Thirty-Fi...· 0 citations
FedRGD is a federated risk-guided dynamic defense framework that enables efficient fine-grained protection against backdoor attacks in non-IID environments, and combines feature inconsistency detection with lightweight masking and robust aggregation to achieve both accuracy and efficiency.
Rui-Ying Wang· Poster Volume 0008 The 2026...· 0 citations
A novel backdoor attack method, termed Federated Generative Adversarial Trigger (FedGAT), which adopts a Generative Adversarial Network (GAN) framework, and can automatically produce optimized triggers that are highly correlated with the global model’s feature space, effectively reducing the “loss” in backdoor transfer...
Tao Liu, Ji-Guang Lv, D. Man et al.· Cybersecurity· 0 citations
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