Xenia Konti

Xenia Konti, Yi Shen, Zifan Wang et al.

The performance of machine learning (ML) models critically depends on the quality and representativeness of the training data. In applications with multiple heterogeneous data generating sources, standard ML methods often learn spurious correlations that perform well on average but degrade performance for atypical or underrepresented groups. Prior work addresses this issue by optimizing the worst-group performance. However, these approaches typically assume that the underlying data distributions for each group can be accurately estimated using the training data, a condition that is frequently violated in noisy, non-stationary, and evolving environments. In this work, we propose a novel framework that relies on Wasserstein-based distributionally robust optimization (DRO) to account for the distributional uncertainty within each group, while simultaneously preserving the objective of improving the worst-group performance. We develop a gradient descent-ascent algorithm to solve the proposed DRO problem and provide convergence results. Finally, we validate the effectiveness of our method on real-world data.

Zifan Wang, Xinlei Yi, Xenia Konti et al.

Federated learning (FL) enables collaborative model training without direct data sharing, but its performance can degrade significantly in the presence of data distribution perturbations. Distributionally robust optimization (DRO) provides a principled framework for handling this by optimizing performance against the worst-case distributions within a prescribed ambiguity set. However, existing DRO-based FL methods often overlook the detrimental impact of outliers in local datasets, which can disproportionately bias the learned models. In this work, we study distributionally robust federated learning with explicit outlier resilience. We introduce a novel ambiguity set based on the unbalanced Wasserstein distance, which jointly captures geometric distributional shifts and incorporates a non-geometric Kullback--Leibler penalization to mitigate the influence of outliers. This formulation naturally leads to a challenging min--max--max optimization problem. To enable decentralized training, we reformulate the problem as a tractable Lagrangian penalty optimization, which admits robustness certificates. Building on this reformulation, we propose the distributionally outlier-robust federated learning algorithm and establish its convergence guarantees. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of our approach.