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Related Experiment Videos

Evaluating multi-level membership inference risk in federated EEG learning.

Taslima Khanam1, Siuly Siuly2, Kate Wang3

  • 1Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC, Australia. taslima.khanam@live.vu.edu.au.

Brain Informatics
|June 21, 2026
PubMed
Summary

Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

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Federated learning (FL) for electroencephalography (EEG) brain-computer interfaces (BCIs) offers some privacy but requires explicit mechanisms like differential privacy (DP) to prevent sensitive data leakage from multiple attacks.

Area of Science:

  • Neuroscience and Artificial Intelligence
  • Focuses on the intersection of brain-computer interfaces (BCIs), electroencephalography (EEG) signal processing, and privacy-preserving machine learning techniques.

Background:

  • Electroencephalography (EEG) is crucial for brain-computer interface (BCI) systems, but its neural recordings contain sensitive personal information.
  • Federated learning (FL) enables collaborative training of BCI models without centralizing raw EEG data, offering a privacy-enhancing approach.
  • Existing research indicates that FL models can still be vulnerable to privacy breaches via membership inference attacks (MIAs).

Purpose of the Study:

  • To investigate the extent of privacy leakage in federated motor-imagery EEG (MI-EEG) classification systems when subjected to multiple types of membership inference attacks (MIAs).
  • To evaluate the effectiveness of differential privacy (DP) in mitigating multi-level privacy risks within FL-based EEG systems.
Keywords:
Brain–computer interfaceElectroencephalographyFederated learningMembership inference attackPrivacy preservation

Related Experiment Videos

  • To determine the optimal balance between privacy protection and classification utility when applying DP to FL for EEG data.

    • Main Methods:

    • Developed a federated motor-imagery EEG (MI-EEG) classification framework using two neural networks trained via per-subject FL.
    • Evaluated privacy leakage using four complementary MIAs: record-level, feature-level, gradient-level, and client-identity inference.
    • Applied differential privacy (DP) with varying epsilon (ε) values (1, 5, 10) to client updates during the federated training process.

    Main Results:

    • Standard federated learning (FL) alone provides minimal intrinsic privacy protection against the evaluated MIAs.
    • Incorporating differential privacy (DP) significantly reduced attack success rates, particularly for gradient-level and client-identity inference attacks.
    • Stronger DP settings (ε=1) maximized privacy but reduced classification accuracy, while moderate settings (ε=5) offered the best privacy-utility trade-off.

    Conclusions:

    • Federated learning (FL) is insufficient as a standalone privacy safeguard for EEG-BCI systems due to multi-level leakage risks.
    • Explicit privacy-enhancing mechanisms, such as differential privacy (DP), are essential for mitigating these risks effectively.
    • The findings support the development of more trustworthy and secure neural-learning technologies by highlighting the need for robust privacy measures in EEG-BCI applications.