Proceedings

Federated Learning at the Forefront of Fairness: A Multifaceted Perspective

Noorain Mukhtiar, Adnan Mahmood, Yipeng Zhou, Jian Yang, Jing Teng, Quan Z. Sheng

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Survey Track. Pages 10603-10611.

https://doi.org/10.24963/ijcai.2025/1177

Keywords

  • Machine Learning: ML: Federated learning
  • AI Ethics, Trust, Fairness: ETF: Fairness and diversity
  • AI Ethics, Trust, Fairness: ETF: Trustworthy AI
  • Machine Learning: General
  • Data Mining: DM: Parallel, distributed and cloud-based high performance mining

Abstract

Fairness in Federated Learning (FL) is emerging as a critical factor driven by heterogeneous clients’ constraints and balanced model performance across various scenarios. In this survey, we delineate a comprehensive classification of the state-of-the-art fairness-aware approaches from a multifaceted perspective, i.e., model performance-oriented and capability-oriented. Moreover, we provide a framework to categorize and address various fairness concerns and associated technical aspects, examining their effectiveness in balancing equity and performance within FL frameworks. We further examine several significant evaluation metrics leveraged to measure fairness quantitatively. Finally, we explore exciting open research directions and propose prospective solutions that could drive future advancements in this important area, laying a solid foundation for researchers working toward fairness in FL.

Noorain Mukhtiar, Adnan Mahmood, Yipeng Zhou, Jian Yang, Jing Teng, and Quan Z. Sheng. 2025. Federated Learning at the Forefront of Fairness: A Multifaceted Perspective. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 10603–10611. https://doi.org/10.24963/ijcai.2025/1177
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