Federated Learning with Relative Fairness

This paper proposes a federated learning framework designed to achieve \textit{relative fairness} for clients. Traditional federated learning frameworks typically ensure absolute fairness by guaranteeing minimum performance across all client subgroups. However, this approach overlooks disparities in model performance between subgroups. The proposed framework uses a minimax problem approach to minimize relative unfairness, extending previous methods in distributionally robust optimization (DRO). A novel fairness index, based on the ratio between large and small losses among clients, is introduced, allowing the framework to assess and improve the relative fairness of trained models. Theoretical guarantees demonstrate that the framework consistently reduces unfairness. We also develop an algorithm, named \textsc{Scaff-PD-IA}, which balances communication and computational efficiency while maintaining minimax-optimal convergence rates. Empirical evaluations on real-world datasets confirm its effectiveness in maintaining model performance while reducing disparity.

Semi-Supervised Approach for Early Stuck Sign Detection in Drilling Operations

A real-time stuck pipe prediction methodology is proposed in this paper. We assume early signs of stuck pipe to be apparent when the drilling data behavior deviates from that from normal drilling operations. The definition of normalcy changes with drill string configuration or geological conditions. Here, a depth-domain data representation is adopted to capture the localized normal behavior. Several models, based on auto-encoder and variational auto-encoders, are trained on regular drilling data extracted from actual drilling data. When the trained model is applied to data sets before stuck incidents, eight incidents showed large reconstruction errors. These results suggest better performance than the previously reported supervised approach. Inter-comparison of various models reveals the robustness of our approach. The model performance depends on the featured parameter suggesting the need for multiple models in actual operation.