The Rashomon set captures the collection of models that achieve near-identical empirical performance yet may differ substantially in their decision boundaries. Understanding the differences among these models, i.e., their multiplicity, is recognized as a crucial step toward model transparency, fairness, and robustness, as it reveals decision boundaries instabilities that standard metrics obscure. However, the existing definitions of Rashomon set and multiplicity metrics assume centralized learning and do not extend naturally to decentralized, multi-party settings like Federated Learning (FL). In FL, multiple clients collaboratively train models under a central server's coordination without sharing raw data, which preserves privacy but introduces challenges from heterogeneous client data distribution and communication constraints. In this setting, the choice of a single best model may homogenize predictive behavior across diverse clients, amplify biases, or undermine fairness guarantees. In this work, we provide the first formalization of Rashomon sets in FL.First, we adapt the Rashomon set definition to FL, distinguishing among three perspectives: (I) a global Rashomon set defined over aggregated statistics across all clients, (II) a t-agreement Rashomon set representing the intersection of local Rashomon sets across a fraction t of clients, and (III) individual Rashomon sets specific to each client's local distribution.Second, we show how standard multiplicity metrics can be estimated under FL's privacy constraints. Finally, we introduce a multiplicity-aware FL pipeline and conduct an empirical study on standard FL benchmark datasets. Our results demonstrate that all three proposed federated Rashomon set definitions offer valuable insights, enabling clients to deploy models that better align with their local data, fairness considerations, and practical requirements.
John A. Paulson School of Engineering and Applied Sciences, Harvard University（哈佛大学约翰·A·保尔森工程与应用科学学院）
Predictive multiplicity occurs when classification models with statistically indistinguishable performances assign conflicting predictions to individual samples. When used for decision-making in applications of consequence (e.g., lending, education, criminal justice), models developed without regard for predictive multiplicity may result in unjustified and arbitrary decisions for specific individuals. We introduce a new metric, called Rashomon Capacity, to measure predictive multiplicity in probabilistic classification. Prior metrics for predictive multiplicity focus on classifiers that output…
Be Intentional About Fairness!: Fairness, Size, and Multiplicity in the Rashomon Set
New York University（纽约大学）；NYU Courant Institute of Mathematical Sciences（纽约大学柯朗数学科学研究所）；Robert F. Wagner Graduate School of Public Service, New York University（纽约大学罗伯特·F·瓦格纳公共服务研究生院）；Tandon School of Engineering, New York University（纽约大学坦登工程学院）；Center for Urban Science and Progress, Tandon School of Engineering, New York University（纽约大学坦登工程学院城市科学与进步中心）；Center for Data Science, New York University（纽约大学数据科学中心）
An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced Classification
Eskisehir Technical University, Department of Statistics, Turkiye（埃斯基谢普技术大学统计系，土耳其）；Warsaw University of Technology, Faculty of Mathematics and Information Science（华沙技术大学数学与信息科学系）；University of Warsaw, Faculty of Mathematics, Informatics and Mechanics（华沙大学数学、信息学与力学系）
Horizon-Constrained Rashomon Sets for Chaotic Forecasting
Dept. of Electrical Engineering, California State University Long Beach, USA；WorkOnward Inc., USA；Dept. of Computer Engineering \& Computer Science, California State University Long Beach, USA
Beyond the Single-Best Model: Rashomon Partial Dependence Profile for Trustworthy Explanations in AutoML
Department of Statistics, Eskisehir Technical University, Turkiye（埃斯基谢普大学统计系）；Leiden Institute of Advanced Computer Science, Leiden University, the Netherlands（莱顿大学高级计算机科学研究所）；Faculty of Mathematics and Information Science, Warsaw University of Technology, Poland（华沙理工大学数学与信息科学学院）；Informatics and Mechanics, University of Warsaw, Faculty of Mathematics, Poland（华沙大学信息技术与力学系）
REALITrees: Rashomon Ensemble Active Learning for Interpretable Trees
Department of Statistics, University of Washington（统计学系，华盛顿大学）；Department of Computer Science, Duke University（计算机科学系，杜克大学）；Department of Sociology, University of Washington（社会学系，华盛顿大学）
CASHomon Sets: Efficient Rashomon Sets Across Multiple Model Classes and their Hyperparameters
Department of Statistics, LMU Munich（统计系，慕尼黑大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；TU Dortmund University（多特蒙德技术大学）；Lamarr Institute for Machine Learning（拉马尔机器学习研究所）
