Federated learning (FL) is a distributed learning framework that leverages commonalities between distributed client datasets to train a global model.

V arious dedicated algorithms have been recently proposed and studied by the machine-learning community. In contrast, active bipartite ranking rule is poorly documented in the literature. Due to its global nature, a strategy for labeling sequentially data points that are difficult to rank w.r.t. to the others is

WhenoptimizedforOOD accuracy, the ensemble model exhibits a noticeable decline in ID accuracy, and vice versa. In contrast, we propose a sample-wise ensembling technique that can simultaneously attain the best ID and OOD accuracywithout the trade-offs.

The largest eigenvalue of the Hessian, or sharpness, of neural networks is a key quantity to understand their optimization dynamics.

Non-convex optimization is a difficult and crucial task.

Providing guarantees on the decision-making processes of autonomous systems, often based on complex black-box machine learning models, is paramount for their safe deployment. This need motivates efforts towards responsible artificial intelligence, which broadly entails questions of reliability, robustness, fairness, and interpretability.