Fairness-Constrained Optimization Attack in Federated Learning

Exploring foci of: arXiv (Cornell University) Fairness-Constrained Optimization Attack in Federated Learning October 2025 • Harsh Kasyap, Minghong Fang, Zhuqing Liu, Carsten Maple, Somanath Tripathy Federated learning (FL) is a privacy-preserving machine learning technique that facilitates collaboration among participants across demographics. FL enables model sharing, while restricting the movement of data. Since FL provides participants with independence over their training data, it becomes susceptible to poisoning attacks. Such collaboration also propagates bias among the participants, even unintentionally, due to different data distribution or historical bias present in the data. This paper proposes an int… Open Article Page

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