Authors

* External authors

Venue

Date

Share

Data Poisoning Attacks on Federated Machine Learning

Gan Sun*

Yang Cong*

Jiahua Dong*

Qiang Wang*

Lingjuan Lyu

Ji Liu*

* External authors

IEEE Internet of Things Journal

2021

Abstract

Federated machine learning which enables resource-constrained node devices (e.g., Internet of Things (IoT) devices, smartphones) to establish a knowledge-shared model while keeping the raw data local, could provide privacy preservation and economic benefit by designing an effective communication protocol. However, this communication protocol can be adopted by attackers to launch data poisoning attacks for different nodes, which has been shown as a big threat to most machine learning models. Therefore, we in this paper intend to study the model vulnerability of federated machine learning, and even on IoT systems. To be specific, we here attempt to attacking a popular federated multi-task learning framework, which uses a general multi-task learning framework to handle statistical challenges in federated learning setting. The problem of calculating optimal poisoning attacks on federated multi-task learning is formulated as a bilevel program, which is adaptive to arbitrary selection of target nodes and source attacking nodes. We then propose a novel systems-aware optimization method, called as ATTack on Federated Learning (ATFL), to efficiently derive the implicit gradients for poisoned data, and further attain optimal attack strategies in the federated machine learning. This is an earlier work, to our knowledge, that explores attacking federated machine learning via data poisoning. Finally, experiments on several real-world datasets demonstrate that when the attackers directly poison the target nodes or indirectly poison the related nodes via using the communication protocol, federated multi-task learning model is sensitive to both poisoning attacks.

Related Publications

How to Evaluate and Mitigate IP Infringement in Visual Generative AI?

ICML, 2025
Zhenting Wang, Chen Chen, Vikash Sehwag, Minzhou Pan*, Lingjuan Lyu

The popularity of visual generative AI models like DALL-E 3, Stable Diffusion XL, Stable Video Diffusion, and Sora has been increasing. Through extensive evaluation, we discovered that the state-of-the-art visual generative models can generate content that bears a striking r…

Six-CD: Benchmarking Concept Removals for Benign Text-to-image Diffusion Models

CVPR, 2025
Jie Ren, Kangrui Chen, Yingqian Cui, Shenglai Zeng, Hui Liu, Yue Xing, Jiliang Tang, Lingjuan Lyu

Text-to-image (T2I) diffusion models have shown exceptional capabilities in generating images that closely correspond to textual prompts. However, the advancement of T2I diffusion models presents significant risks, as the models could be exploited for malicious purposes, suc…

CO-SPY: Combining Semantic and Pixel Features to Detect Synthetic Images by AI

CVPR, 2025
Siyuan Cheng, Lingjuan Lyu, Zhenting Wang, Xiangyu Zhang, Vikash Sehwag

With the rapid advancement of generative AI, it is now pos-sible to synthesize high-quality images in a few seconds.Despite the power of these technologies, they raise signif-icant concerns regarding misuse. Current efforts to dis-tinguish between real and AI-generated image…

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.