CurvFed: Curvature-Aligned Federated Learning for Fairness without Demographics

Modern human-sensing applications often rely on data distributed across users and devices, where privacy concerns prevent centralized training. Federated Learning (FL) addresses this challenge by enabling collaborative model training without exposing raw data or attributes. However, achieving fairness in such settings remains difficult, as most human-sensing datasets lack demographic labels, and FL's privacy guarantees limit the use of sensitive attributes. This paper introduces CurvFed: Curvature-Aligned Federated Learning for Fairness without Demographics, a theoretically grounded framework that promotes fairness in FL without requiring any demographic or sensitive attribute information—a concept termed Fairness without Demographics (FWD)— by optimizing the underlying loss-landscape curvature. Building on the theory that equivalent loss-landscape curvature corresponds to consistent model efficacy across sensitive attribute groups, CurvFed regularizes the top eigenvalue of the Fisher Information Matrix (FIM) as an efficient proxy for loss-landscape curvature, both within and across clients. This alignment promotes uniform model behavior across diverse bias-inducing factors, offering an attribute-agnostic route to algorithmic fairness. CurvFed is especially suitable for real-world human-sensing FL scenarios involving single or multi-user edge devices with unknown or multiple bias factors. We validated CurvFed through theoretical and empirical justifications, as well as comprehensive evaluations using four real-world datasets and a deployment on a heterogeneous testbed of resource-constrained devices. Additionally, we conduct sensitivity analyses on local training data volume, client sampling, communication overhead, resource costs, and runtime performance to demonstrate its feasibility for practical FL edge device deployment. The implementation of CurvFed is publicly available at: Github-repo