The paper introduces soft‑label‑based estimators for the Bayes‑optimal balanced error rate (BER) and area under the ROC curve (AUC), extending from a clean setting with known class priors to a realistic scenario with unknown priors and corrupted soft labels. It also adapts the FeeBee evaluation framework to assess these estimators without needing the true optimum, providing practical evaluation scores for any estimator of optimal BER or AUC. Experiments on synthetic and real datasets confirm the effectiveness of both the estimators and the evaluation method.
By Ryota Ushio, Takashi Ishida, Masashi Sugiyama
arXiv:2608. 09074v1 Announce Type: cross Abstract: We develop a new approach to Personalized Federated Learning across heterogeneous clients using Nonparametric Empirical Bayes (NPEB).
By Jae Ho Chang, Arnab Auddy, Subhadeep Paul
FedFIbOS introduces a Fisher‑importance based criterion for selecting submodel parameters in heterogeneous federated learning, addressing the lack of theoretical justification in prior heuristic methods. By deriving a Fisher‑weighted quadratic masking surrogate and showing that the raw Fisher top‑k rule satisfies this surrogate under a Fisher‑dominant ranking condition, the method preserves convergence guarantees while efficiently estimating Fisher scores from squared gradients. Experiments on CIFAR‑10, CIFAR‑100, and AGNews demonstrate that FedFIbOS outperforms state‑of‑the‑art approaches by roughly 10% in accuracy, especially under strong non‑IID heterogeneity.
By Yasmeen Afzal, Jeremiah D. Deng, Haibo Zhang
arXiv:2606. 28835v1 Announce Type: cross Abstract: Federated Learning (FL) emerged as a promising distributed machine learning paradigm.
By Wenhao Yuan, Chenchen Lin, Jian Chen, Jinfeng Xu, Zewei Liu, Edith Cheuk Han Ngai
arXiv:2407. 12288v5 Announce Type: replace-cross Abstract: The progress of machine learning over the past decade is undeniable.
By Hong Jun Jeon, Benjamin Van Roy
arXiv:2607. 24943v1 Announce Type: cross Abstract: In many classification problems, reliable instance-level labels are unavailable.
By Rapha\"el Bonnet-Guerrini, Johann Ioannou-Nikolaides, Troels Petersen, Vincenzo Piuri