Work

Fairness-aware Federated Minimax Optimization with Convergence Guarantee

Year: 2024

Type: conference-paper

Abstract: Federated learning (FL) has garnered considerable attention due to its privacy-preserving feature. Nonetheless, the lack of freedom in managing user data can lead to group fairness issues, where models are biased towards sensitive factors such as race or gender. To tackle this issue, this paper proposes a novel algorithm, fair federated averaging with augmented Lagrangian method (FFALM), designed explicitly to address group fairness issues in FL. Specifically, we impose a fairness constraint on the training objective and solve the minimax reformulation of the constrained optimization problem. Then, we derive the theoretical upper bound for the convergence rate of FFALM. The effectiveness of FFALM in improving fairness is shown empirically on CelebA and UTKFace datasets in the presence of severe statistical heterogeneity.

Source: IEEE Conference on Artificial Intelligence (CAI)

DOI: 10.1109/cai59869.2024.00111

Authors: Gerry Windiarto Mohamad Dunda, Shenghui Song

Institution: Hong Kong University of Science and Technology

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Reduced inequalities

Open Access status: green

Funders: National Natural Science Foundation of China, Hong Kong University of Science and Technology