mlsanityData to 7 Oct 2026

OCT-FedSIR: Toward Trustworthy Federated Ophthalmic Learning under Annotation Noise

arXiv 2609.14734 ↗First posted 13 Sep 2026cs.LG cs.AI cs.DCcited by 0

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Abstract (arXiv)

Federated learning enables collaborative model development without centralizing patient data, but annotation reliability at participating institutions cannot always be assumed. In ophthalmic imaging, differences in disease prevalence and class composition can resemble changes caused by corrupted supervision. We introduce OCT-FedSIR, a reliability-aware spectral framework for federated OCT classification under client-dependent annotation noise and heterogeneous data distributions. OCT-FedSIR combines class-balanced spectral estimation, Stage-I logit adjustment, complementary spectral descriptors, selective spectral relabeling, and noise-aware federated optimization. We evaluated the framework on the Kermany, University of Illinois Chicago, and Wake Forest datasets under symmetric and structured asymmetric noise and three levels of non-IID heterogeneity. Across 117 experimental conditions, OCT-FedSIR achieved a mean accuracy of 86.73%, compared with 79.94% for RoFL and 78.75% for FedCorr. It correctly separated clients with original and corrupted annotations across all evaluated conditions, while the original FedSIR identification procedure was less robust, particularly under asymmetric noise. Spectral relabeling recovered 77.2% of corrupted annotations with 91.3% correction precision and a 3.5% false-correction rate. Retaining corrected clients outperformed spectral pruning by 9.30 percentage points on average. These findings show that annotation noise can often be identified and corrected without discarding informative client data.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 36 references that are in the map. First posted on arXiv 13 Sep 2026.

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References · 16

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2018ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designcited by 1,132
  4. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,054
  5. 2020Designing Network Design Spacescited by 954
  6. 2019Towards Federated Learning at Scale: System Designcited by 826
  7. 2019Deep learning with noisy labels: exploring techniques and remedies in medical image analysiscited by 173
  8. 2022FedCorr: Multi-Stage Federated Learning for Label Noise Correctioncited by 61
  9. 2020Robust Federated Learning with Noisy Labelscited by 40
  10. 2023FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneitycited by 33
  11. 2022Towards Federated Learning against Noisy Labels via Local Self-Regularizationcited by 26
  12. 2023Federated Learning with Extremely Noisy Clients via Negative Distillationcited by 12
4 more
  1. 2024Tackling Noisy Clients in Federated Learning with End-to-end Label Correctioncited by 11
  2. 2026FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learningcited by 2
  3. 2024Multi-OCT-SelfNet: Integrating Self-Supervised Learning with Multi-Source Data Fusion for Enhanced Multi-Class Retinal Disease Classificationcited by 1
  4. 2026FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labelscited by 1

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