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Related Experiment Videos

User Isolation Poisoning on Decentralized Federated Learning: An Adversarial Message-Passing Graph Neural Network

Kai Li, Yilei Liang, Pietro Lio

    IEEE Transactions on Neural Networks and Learning Systems
    |December 2, 2025
    PubMed
    Summary
    This summary is machine-generated.

    A new cyberattack, user isolation poisoning (UIP), targets decentralized federated learning (DFL) by isolating benign model updates. This attack significantly reduces DFL accuracy and evades current defenses.

    Related Experiment Videos

    Area of Science:

    • Cybersecurity
    • Machine Learning
    • Decentralized Systems

    Background:

    • Decentralized federated learning (DFL) enables collaborative model training without centralizing data.
    • Ensuring the integrity and security of DFL against malicious participants is a critical challenge.

    Purpose of the Study:

    • To introduce a novel cyberattack, user isolation poisoning (UIP), designed to undermine DFL.
    • To demonstrate the effectiveness of UIP in reducing the accuracy of DFL models.

    Main Methods:

    • A malicious user strategically crafts and distributes compromised model updates within the DFL protocol.
    • An adversarial message-passing graph (MPG) neural network is employed to isolate benign model updates by refining their representations.
    • Targeted feature exchanges within the MPG manipulate benign model updates, diminishing their influence.

    Main Results:

    • The proposed user isolation poisoning attack effectively reduces the test accuracy of DFL by 49.5%.
    • The MPG-based UIP attack successfully evades existing defense strategies based on cosine similarity and Euclidean distance.

    Conclusions:

    • User isolation poisoning presents a significant threat to the integrity of decentralized federated learning.
    • Novel defense mechanisms are required to counter advanced attacks like UIP that manipulate model updates.