Globally coordinated sample reweighting for fair federated learning under heterogeneity

Federated learning (FL) enables collaborative model training across decentralized institutions, making group fairness an important requirement when the resulting global model is deployed across demographically heterogeneous users. However, assessing global group fairness requires statistics aggregated across clients, whereas FL updates the model using private local data. This inherent mismatch makes it challenging to guarantee global group fairness, especially under client heterogeneity. To address this problem, we introduce GloReFair, a globally coordinated sample reweighting framework that bridges this gap by converting global fairness feedback into local training signals. The key idea is to separate where fairness correction is needed from how training emphasis should be allocated. We realize this separation through a server-side controller that adjusts channel prices using global fairness residuals. Given these prices, clients compute sample weights using a unique closed-form rule derived from the Lagrangian of an idealized round-wise allocation problem. We further establish theoretical guarantees on centralized equivalence and strict controller expressivity. Across three federated benchmarks, GloReFair achieves favorable fairness-utility tradeoffs and remains robust as client heterogeneity becomes severe, including settings in which competing fair-FL methods substantially degrade.