mlsanityData to 14 Oct 2026

Different Corruptions, Different Signals: Uncertainty and Loss in Federated Data Quality

arXiv 2609.31454 ↗First posted 25 Sep 2026cs.LG cs.AI cs.CVcited by 0

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Abstract (arXiv)

Federated learning (FL) data corruption can affect either inputs or labels, but it remains unclear whether input-conditional uncertainty and prediction-label loss expose these corruption modes equally. This paper compares two corruption-detection signals in FL: input-conditional uncertainty and prediction-label loss. The uncertainty signal is characterised using a learned aleatoric variance estimate together with Monte Carlo (MC) dropout variance and entropy measures, while the loss is computed against the supplied label. We test these signals against additive image noise and persistent random label flips. On ResNet-20 with CIFAR-10 and SVHN under Dirichlet partitions with data that are not independent and identically distributed (non-IID), the two corruption types behave differently. For persistent random label flips, the within-client per-sample area under the receiver operating characteristic curve (AUC) is 0.85 on CIFAR-10 and 0.95 on SVHN for prediction-label loss, while every uncertainty estimator stays at chance (0.49--0.50). This pattern is consistent with the model remaining confident in the underlying image despite the supplied label being wrong. For image noise, expected-entropy uncertainty rises above chance (0.67 on CIFAR-10 and 0.66 on SVHN), while loss responds comparably (0.64 on both). Each signal is therefore the stronger detector for a different corruption: the prediction-label loss for persistent label flips, and expected-entropy uncertainty for image noise, with its advantage becoming apparent as federation-wide corruption prevalence increases. Robust FL data-quality assessment should match the signal to the corruption rather than rely on uncertainty alone across corruption types.

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References · 17

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  10. 2023Federated Learning with Extremely Noisy Clients via Negative Distillationcited by 12
  11. 2023Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networkscited by 8
  12. 2025FNBench: Benchmarking Robust Federated Learning against Noisy Labelscited by 6
5 more
  1. 2024Improving Label Error Detection and Elimination with Uncertainty Quantificationcited by 4
  2. 2023SureFED: Robust Federated Learning via Uncertainty-Aware Inward and Outward Inspectioncited by 3
  3. 2024Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimizationcited by 2
  4. 2025Achieving Distributive Justice in Federated Learning via Uncertainty Quantificationcited by 2
  5. 2025UDF-GMA: Uncertainty Disentanglement and Fusion for General Movement Assessmentcited by 2

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