FNBench: Benchmarking Robust Federated Learning against Noisy Labels | Paper Citation Record

Observation e3b69c5e-eda0-4335-a521-4dd33f5e290f · outbound

This paper cites Tackling noisy clients in federated learning with end-to-end label correction,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Tackling noisy clients in federated learning with end-to-end label correction,

Reference 1

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This paper cites Federated learning: Challenges, methods, and future directions,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Federated learning: Challenges, methods, and future directions,

Reference 2

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Observation 0ffbd01c-ad87-40c4-9243-08c498bba58b · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Communication-efficient learning of deep networks from decentralized data,

Reference 3

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This paper cites Fedeye: A scalable and flexible end-to-end federated learning platform for ophthalmology,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Fedeye: A scalable and flexible end-to-end federated learning platform for ophthalmology,

Reference 5

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Observation 407799a5-3e7f-4578-9218-5dc4f1d8234b · outbound

This paper cites Fednoro: Towards noise-robust federated learning by addressing class imbalance and label noise heterogeneity,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Fednoro: Towards noise-robust federated learning by addressing class imbalance and label noise heterogeneity,

Reference 6

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Observation 75ba35e3-c63b-40c3-8288-4e5e2677e31e · outbound

This paper cites A knowledge transfer- based semi-supervised federated learning for iot malware detection,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels A knowledge transfer- based semi-supervised federated learning for iot malware detection,

Reference 7

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This paper cites Spotting anomalies at the edge: Outlier exposure-based cross-silo federated learning for ddos detection,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Spotting anomalies at the edge: Outlier exposure-based cross-silo federated learning for ddos detection,

Reference 8

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This paper cites Fedict: Federated multi-task distillation for multi-access edge computing,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Fedict: Federated multi-task distillation for multi-access edge computing,

Reference 10

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Observation 396a8771-5031-4023-b257-a3cce67e0ca7 · outbound

This paper cites Heterogeneous Federated Learning: State-of-the-art and Research Challenges.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Heterogeneous Federated Learning: State-of-the-art and Research Challenges

Reference 12

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Observation 8ba40a47-6211-4fe8-9172-0ace40452c4c · outbound

This paper cites Fedbiad: Communication-efficient and accuracy-guaranteed federated learning with bayesian inference-based adaptive dropout,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Fedbiad: Communication-efficient and accuracy-guaranteed federated learning with bayesian inference-based adaptive dropout,

Reference 13

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Observation 936f42ac-835e-4862-8a6c-b7d0b8bfb36a · outbound

This paper cites Knowledge Distillation in Federated Edge Learning: A Survey.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Knowledge Distillation in Federated Edge Learning: A Survey

Reference 14

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Observation a8c5f121-14ed-4ba3-8662-df350a2b0481 · outbound

This paper cites Understanding and mitigating dimensional collapse in federated learning,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Understanding and mitigating dimensional collapse in federated learning,

Reference 15

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Observation 9473529f-8a99-4328-9914-688261cd9de9 · outbound

This paper cites CLC: A consensus-based label correction approach in federated learning,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels CLC: A consensus-based label correction approach in federated learning,

Reference 16

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Observation 2d3e9062-85d9-4a25-9625-f5f72589cbab · outbound

This paper cites FOCUS: Dealing with Label Quality Disparity in Federated Learning.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels FOCUS: Dealing with Label Quality Disparity in Federated Learning

Reference 17

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Observation d84b8636-8a2b-4ed2-ac64-4ac6b1d8bf7a · outbound

This paper cites Robust federated learning with noisy labels,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Robust federated learning with noisy labels,

Reference 18

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This paper cites Fedrn: Exploiting k-reliable neighbors towards robust federated learning,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Fedrn: Exploiting k-reliable neighbors towards robust federated learning,

Reference 20

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Observation 7db3dc54-3eb9-418c-8654-d0c0e9778641 · outbound

This paper cites Co-teaching: Robust training of deep neural networks with extremely noisy labels,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Co-teaching: Robust training of deep neural networks with extremely noisy labels,

Reference 21

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Observation bd403451-8e76-4bef-9936-cee8838cecd7 · outbound

This paper cites Federated learning on non-iid data silos: An experimental study,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Federated learning on non-iid data silos: An experimental study,

Reference 22

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Observation ed94dad0-ca7c-438d-a19b-eb5d6158d83e · outbound

This paper cites Deep residual learning for image recognition,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Deep residual learning for image recognition,

Reference 23

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Observation d1c753cb-8340-4f13-984f-884988351665 · outbound

This paper cites Learning multiple layers of features from tiny images,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Learning multiple layers of features from tiny images,

Reference 24

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Observation 1ac77d1b-5330-45bc-b072-a9ceea16182e · outbound

This paper cites Fedcrac: Improving federated classification performance on long-tailed data via classifier representation adjustment and calibration,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Fedcrac: Improving federated classification performance on long-tailed data via classifier representation adjustment and calibration,

Reference 25

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Observation b7c57f4c-818b-4c76-90c2-bd811fff6aac · outbound

This paper cites Federated learning on non-iid data: A survey,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Federated learning on non-iid data: A survey,

Reference 26

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This paper cites Agglomerative federated learning: Empowering larger model training via end-edge-cloud collaboration,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Agglomerative federated learning: Empowering larger model training via end-edge-cloud collaboration,

Reference 28

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Observation 6312ce50-5370-4d48-80ce-3f1b8067fb35 · outbound

This paper cites Federated Skewed Label Learning with Logits Fusion.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Federated Skewed Label Learning with Logits Fusion

Reference 29

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This paper cites Fedlf: Adaptive logit adjustment and feature optimization in federated long-tailed learning,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Fedlf: Adaptive logit adjustment and feature optimization in federated long-tailed learning,

Reference 30

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This paper cites Federated optimization in heterogeneous networks,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Federated optimization in heterogeneous networks,

Reference 31

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This paper cites Fedexp: Speeding up federated averaging via extrapolation,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Fedexp: Speeding up federated averaging via extrapolation,

Reference 32

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This paper cites Mopro: Webly supervised learning with momentum prototypes,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Mopro: Webly supervised learning with momentum prototypes,

Reference 33

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This paper cites Learning from massive noisy labeled data for image classification,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Learning from massive noisy labeled data for image classification,

Reference 34

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This paper cites The open images dataset V4,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels The open images dataset V4,

Reference 35

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This paper cites Image- based recommendations on styles and substitutes,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Image- based recommendations on styles and substitutes,

Reference 36

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This paper cites Pervasive label errors in test sets destabilize machine learning benchmarks,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Pervasive label errors in test sets destabilize machine learning benchmarks,

Reference 37

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This paper cites Robustfed: A truth inference approach for robust federated learning,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Robustfed: A truth inference approach for robust federated learning,

Reference 38

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This paper cites Incentive mechanism for horizontal federated learning based on reputation and reverse auction,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Incentive mechanism for horizontal federated learning based on reputation and reverse auction,

Reference 39

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This paper cites Regulation (EU) 2016/679 of the European Parliament and of the Council.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Regulation (EU) 2016/679 of the European Parliament and of the Council

Reference 40

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This paper cites Federated learning with unreliable clients: Performance analysis and mechanism design,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Federated learning with unreliable clients: Performance analysis and mechanism design,

Reference 41

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This paper cites A closer look at memorization in deep networks,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels A closer look at memorization in deep networks,

Reference 42

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This paper cites Learning from noisy labels with deep neural networks: A survey,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Learning from noisy labels with deep neural networks: A survey,

Reference 43

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This paper cites How does disagreement help generalization against label corruption?.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels How does disagreement help generalization against label corruption?

Reference 44

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This paper cites Joint optimization framework for learning with noisy labels,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Joint optimization framework for learning with noisy labels,

Reference 45

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FNBench: Benchmarking Robust Federated Learning against Noisy Labels SELFIE: refurbishing unclean samples for robust deep learning,

Reference 46

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This paper cites Symmetric cross entropy for robust learning with noisy labels,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Symmetric cross entropy for robust learning with noisy labels,

Reference 47

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This paper cites Robust loss functions under label noise for deep neural networks,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Robust loss functions under label noise for deep neural networks,

Reference 48

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This paper cites Generalized cross entropy loss for training deep neural networks with noisy labels,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Generalized cross entropy loss for training deep neural networks with noisy labels,

Reference 49

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This paper cites Dividemix: Learning with noisy labels as semi-supervised learning,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Dividemix: Learning with noisy labels as semi-supervised learning,

Reference 50

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This paper cites mixup: Beyond empirical risk minimization,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels mixup: Beyond empirical risk minimization,

Reference 51

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This paper cites Mixmatch: A holistic approach to semi-supervised learning,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Mixmatch: A holistic approach to semi-supervised learning,

Reference 52

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This paper cites Early-learning regularization prevents memorization of noisy labels,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Early-learning regularization prevents memorization of noisy labels,

Reference 53

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This paper cites Learning with noisy labels,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Learning with noisy labels,

Reference 54

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This paper cites Fedcorr: Multi-stage federated learning for label noise correction,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Fedcorr: Multi-stage federated learning for label noise correction,

Reference 55

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This paper cites Learning cautiously in federated learning with noisy and heterogeneous clients,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Learning cautiously in federated learning with noisy and heterogeneous clients,

Reference 56

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This paper cites On the number of components in a gaussian mixture model,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels On the number of components in a gaussian mixture model,

Reference 57

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This paper cites Federated learning with extremely noisy clients via negative distillation,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Federated learning with extremely noisy clients via negative distillation,

Reference 58

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This paper cites Fedelr: When federated learning meets learning with noisy labels,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Fedelr: When federated learning meets learning with noisy labels,

Reference 59

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This paper cites Agrevader: Poisoning membership inference against byzantine-robust federated learning,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Agrevader: Poisoning membership inference against byzantine-robust federated learning,

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This paper cites Robust aggregation for federated learning,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Robust aggregation for federated learning,

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This paper cites Ditto: Fair and robust federated learning through personalization,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Ditto: Fair and robust federated learning through personalization,

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This paper cites Byzantine-robust distributed learning: Towards optimal statistical rates,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Byzantine-robust distributed learning: Towards optimal statistical rates,

Reference 63

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This paper cites Machine learning with adversaries: Byzantine tolerant gradient descent,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Machine learning with adversaries: Byzantine tolerant gradient descent,

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This paper cites FedScale: Benchmarking model and system performance of federated learning at scale,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels FedScale: Benchmarking model and system performance of federated learning at scale,

Reference 65

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Observation f0a5f8e1-2eca-4f00-a92e-73c91a82145d · outbound

This paper cites FedML: A Research Library and Benchmark for Federated Machine Learning.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels FedML: A Research Library and Benchmark for Federated Machine Learning

Reference 66

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Observation bcf0abc6-ade0-4854-80c4-c7fca4f86e4b · outbound

This paper cites LEAF: A Benchmark for Federated Settings.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels LEAF: A Benchmark for Federated Settings

Reference 67

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Observation fad917eb-4ae1-4050-87bc-897cc0513750 · outbound

This paper cites How asynchronous can federated learning be?.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels How asynchronous can federated learning be?

Reference 68

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Observation 04508d4e-a63c-4c64-bc19-c3bf7e6e6007 · outbound

This paper cites Fedlab: A flexible federated learning framework,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Fedlab: A flexible federated learning framework,

Reference 69

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Observation a4b3eddd-5d40-41ab-83e8-5ab525901644 · outbound

This paper cites FedNoisy: Federated Noisy Label Learning Benchmark.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels FedNoisy: Federated Noisy Label Learning Benchmark

Reference 70

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Observation 4ccb9f7d-173d-4b61-ba48-f04e4412eaea · outbound

This paper cites Communication efficient distributed machine learning with the parameter server,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Communication efficient distributed machine learning with the parameter server,

Reference 71

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Observation 4a3768b9-b2a1-4122-b456-af43accb4a8c · outbound

This paper cites Character-level convolutional networks for text classification,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Character-level convolutional networks for text classification,

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-15T22:39:29.535337Z

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Observation 1a8942a8-ce7b-4bc2-b97d-70fe4057f379 · outbound

This paper cites Learning with noisy labels revisited: A study using real-world human annotations,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Learning with noisy labels revisited: A study using real-world human annotations,

Reference 73

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Observation 01343b49-46c8-48d8-9e4b-087fa6e60de3 · outbound

This paper cites Labeling chaos to learning harmony: Federated learning with noisy labels,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Labeling chaos to learning harmony: Federated learning with noisy labels,

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-15T22:39:29.543413Z

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Observation c93c9ef7-6752-4403-9893-e2edeeaf6dab · outbound

This paper cites Learning with symmetric label noise: The importance of being unhinged,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Learning with symmetric label noise: The importance of being unhinged,

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-15T22:39:29.550853Z

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Observation 3ab78f08-06e8-4657-9407-d0ecb2c2c328 · outbound

This paper cites Security-Preserving Federated Learning via Byzantine-Sensitive Triplet Distance.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Security-Preserving Federated Learning via Byzantine-Sensitive Triplet Distance

Reference 76

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Observation 3ec53a2a-e053-4e04-b7cd-a7bff6924619 · outbound

This paper cites Understanding deep learning requires rethinking generalization,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Understanding deep learning requires rethinking generalization,

Reference 77

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Observation cf69bdbc-1ae6-41b4-84e1-a137c34bfb0f · outbound

This paper cites Understanding dimensional collapse in contrastive self-supervised learning,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Understanding dimensional collapse in contrastive self-supervised learning,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:39:32.326116Z

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Observation e37f78cd-d9aa-46c4-9187-a27edb24febb · outbound

This paper cites Bilayer-induced asymmetric quantum Hall effect in epitaxial graphene.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Bilayer-induced asymmetric quantum Hall effect in epitaxial graphene

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-15T22:39:29.571370Z

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Observation 530f6055-cf0b-49b0-8625-0cc820191fc0 · outbound

This paper cites Model-contrastive federated learning,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Model-contrastive federated learning,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:39:32.314374Z

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Observation 517426ef-5a97-4151-aaee-7059e486bd97 · outbound

This paper cites Correlated-pca: Principal components’ analysis when data and noise are correlated,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Correlated-pca: Principal components’ analysis when data and noise are correlated,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:39:32.304191Z

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Observation b9b99e1f-e7eb-4151-b7ed-1c64667a518c · outbound

This paper cites An O(n3) algorithm for the frobenius normal form,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels An O(n3) algorithm for the frobenius normal form,

Reference 82

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T22:39:30.562373Z

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Observation 7d18b13d-c7b1-4c84-87fc-f3b7e0e7937d · outbound

This paper cites Available: https://doi.org/10.1145/3626242.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Available: https://doi.org/10.1145/3626242

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-15T22:39:29.547005Z

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Observation 8b64b1f7-22d4-453f-b63b-5588eb4f3a35 · outbound

This paper cites Unsupervised ensemble learning with noisy label correction,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Unsupervised ensemble learning with noisy label correction,

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-15T22:39:29.594172Z

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Observation ac6d1de4-5a9e-4036-97ec-7d82c6f67420 · outbound

This paper cites Understanding and utilizing deep neural networks trained with noisy labels,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Understanding and utilizing deep neural networks trained with noisy labels,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:39:32.271595Z

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Observation 0ac8f89b-beaa-4115-bd5b-65f394f6c40f · outbound

This paper cites Security-Preserving Federated Learning via Byzantine-Sensitive Triplet Distance.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Security-Preserving Federated Learning via Byzantine-Sensitive Triplet Distance

Reference 86

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Observation 1b1d3fcc-47ae-4ac0-8e63-32ef3440a62a · outbound

This paper cites Bag of tricks for efficient text classification,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Bag of tricks for efficient text classification,

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-15T22:39:29.607455Z

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Observation 4525e104-664a-4ffa-9510-72674398c8fb · outbound

This paper cites Overcoming noisy labels in federated learning through local self-guiding,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Overcoming noisy labels in federated learning through local self-guiding,

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-15T22:39:29.610823Z

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Observation 87259fae-3943-4e36-90cc-a655fa1787c2 · outbound

This paper cites Self-filtering: A noise-aware sample selection for label noise with confidence penalization,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Self-filtering: A noise-aware sample selection for label noise with confidence penalization,

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-15T22:39:29.614322Z

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Observation af87f71a-44ea-4617-b489-7e5e4a5c8147 · outbound

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FNBench: Benchmarking Robust Federated Learning against Noisy Labels Unresolved cited work

Reference 90

Resolution: verified exact; raw_fallback, observed 2026-08-15T22:39:30.405552Z

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Observation a14c98c1-ebad-4047-b2cd-e07d0515a5eb · outbound

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FNBench: Benchmarking Robust Federated Learning against Noisy Labels Unresolved cited work

Reference 91

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:39:32.238391Z

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Observation 7351f47d-9125-4bab-a167-42350f064935 · outbound

This paper cites Robust heterogeneous federated learning under data corruption,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Robust heterogeneous federated learning under data corruption,

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:39:32.226598Z

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Observation 15ec7e3c-72f0-47a1-9142-5d082a41c5a8 · outbound

This paper cites Quantifying and mitigating the impact of label errors on model disparity metrics,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Quantifying and mitigating the impact of label errors on model disparity metrics,

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:39:32.216461Z

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Observation 000d1dce-c527-4e9b-ab38-5f53c1e3d46c · outbound

This paper cites Paszke, S.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Paszke, S

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:39:32.282696Z

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Observation ffce05e6-b696-4a92-a9f3-55a7fd12dbb4 · outbound

This paper cites FedRGL: Robust Federated Graph Learning for Label Noise.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels FedRGL: Robust Federated Graph Learning for Label Noise

Reference 95

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:39:29.776846Z

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Observation 11c1fa22-fc8d-4c65-bee7-6ce9cbc854a2 · outbound

This paper cites Feda3i: Annotation quality- aware aggregation for federated medical image segmentation against heterogeneous annotation noise,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Feda3i: Annotation quality- aware aggregation for federated medical image segmentation against heterogeneous annotation noise,

Reference 96

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Observation bc47a904-a549-4400-87ee-7be0a3e287ab · outbound

This paper cites Mixed precision training,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Mixed precision training,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:39:32.260376Z

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Observation 04ef8977-7e1d-45f1-8181-81dcce4c9a7b · outbound

This paper cites Available: https://openreview.net/forum?id=r1gs9JgRZ.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Available: https://openreview.net/forum?id=r1gs9JgRZ

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:39:32.249907Z

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Observation 3d8a3d14-67fd-4bdb-ac83-2e77646b4bf4 · outbound

This paper cites Augmentation strategies for learning with noisy labels,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Augmentation strategies for learning with noisy labels,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:39:32.199704Z

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Observation 235dbf75-94b0-4929-b6b6-baad77127ca2 · outbound

This paper cites Sample-level data selection for federated learning,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Sample-level data selection for federated learning,

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-15T22:39:29.661465Z

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Observation 6bb88e30-86a0-48a3-8193-b552535ebeeb · outbound

This paper cites FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels

Reference 101

Resolution: unresolved; no resolver link, observed 2026-08-15T22:39:29.665646Z

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Observation 38c39451-55f7-46a9-bc14-c26ba1bc671f · outbound

This paper cites Smote-variants: A python implementation of 85 minority oversampling techniques,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Smote-variants: A python implementation of 85 minority oversampling techniques,

Reference 102

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:39:32.188833Z

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Observation 4c3f64cf-4936-4388-92c7-5680432da2cf · outbound

This paper cites Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels

Reference 103

Resolution: unresolved; no resolver link, observed 2026-08-15T22:39:29.678169Z

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Observation 149eb866-2f44-4f59-be37-314fd2b8dc5b · outbound

This paper cites Communication-efficient robust federated learning with noisy labels,.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels Communication-efficient robust federated learning with noisy labels,

Reference 104

Resolution: unresolved; no resolver link, observed 2026-08-15T22:39:29.682066Z

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Observation 409a5ff9-af21-4948-b452-ba02e73e258f · outbound

This paper cites 4997–5007.

FNBench: Benchmarking Robust Federated Learning against Noisy Labels 4997–5007

Reference 105

Resolution: unresolved; no resolver link, observed 2026-08-15T22:39:29.628762Z

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