Federated Adversarial Debiasing for Fair and Transferable Representations.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Distributed Systems

Background:

• Federated learning (FL) offers communication efficiency and data privacy.
• User heterogeneity in FL can lead to biased models, particularly against minority groups.
• Extending centralized adversarial debiasing methods to FL faces significant challenges.

Purpose of the Study:

• To propose a novel federated adversarial debiasing (FADE) approach to mitigate bias in federated learning.
• To address the barriers of applying adversarial learning in federated settings.
• To ensure fairness in federated models without requiring sensitive group information.

Main Methods:

• Developed Federated Adversarial DEbiasing (FADE), a novel approach for federated learning.
• FADE allows users to opt-out of the adversarial component for privacy and computational flexibility.
• Analyzed convergence properties and proposed solutions for practical failure cases.

Main Results:

• FADE can achieve global optimality comparable to centralized methods under ideal conditions.
• Empirical studies demonstrate FADE's effectiveness in unsupervised domain adaptation and fair learning.
• The proposed method addresses practical convergence issues in federated adversarial debiasing.

Conclusions:

• FADE provides an effective solution for mitigating bias in federated learning.
• The framework enhances fairness while respecting user privacy and computational constraints.
• FADE shows promise for applications requiring fair and unbiased distributed models.