FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise | Paper Citation Record

Observation cabec715-ea3d-46c8-bd9f-caaf1f8ff037 · outbound

This paper cites Vucinich and Q.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Vucinich and Q

Reference 1

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Observation 0c45c774-2a1d-4a30-bc3e-ecb7af01fe30 · outbound

This paper cites Lancelot: Towards Efficient and Privacy-Preserving Byzantine-Robust Federated Learning within Fully Homomorphic Encryption.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Lancelot: Towards Efficient and Privacy-Preserving Byzantine-Robust Federated Learning within Fully Homomorphic Encryption

Reference 2

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Observation 2d0bbb71-264a-4fce-b01f-c039e807c87a · outbound

This paper cites Can You Really Backdoor Federated Learning?.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Can You Really Backdoor Federated Learning?

Reference 3

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This paper cites Hard sample a ware noise robust learning for histopathology image classiﬁcat ion,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Hard sample a ware noise robust learning for histopathology image classiﬁcat ion,

Reference 4

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This paper cites Mendieta, T.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Mendieta, T

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:32.130609Z

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This paper cites Communication-efﬁcient learning of deep networks from de central- ized data,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Communication-efﬁcient learning of deep networks from de central- ized data,

Reference 6

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

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Federated learning with extremely noisy clients via negative distillation,

Reference 7

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Observation bd58f5c1-3156-4a56-b6ff-589aa01630ec · outbound

This paper cites Tackling Noisy Clients in Federated Learning with End-to-end Label Correction.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Tackling Noisy Clients in Federated Learning with End-to-end Label Correction

Reference 8

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This paper cites FedDiv: Collabo rative noise ﬁltering for federated learning with noisy labels,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise FedDiv: Collabo rative noise ﬁltering for federated learning with noisy labels,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:31.336709Z

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This paper cites A systematic stu dy of the class imbalance problem in convolutional neural networ ks,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise A systematic stu dy of the class imbalance problem in convolutional neural networ ks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:31.034748Z

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This paper cites an unresolved cited work.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Unresolved cited work

Reference 11

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This paper cites Understand- ing deep learning (still) requires rethinking generalizat ion,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Understand- ing deep learning (still) requires rethinking generalizat ion,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:30.413934Z

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This paper cites On the robustnes s of decision tree learning under label noise,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise On the robustnes s of decision tree learning under label noise,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:30.288215Z

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This paper cites Loss fa ctorization, weakly supervised learning and label noise robustness,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Loss fa ctorization, weakly supervised learning and label noise robustness,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:30.118621Z

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This paper cites A Survey of Label-noise Representation Learning: Past, Present and Future.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise A Survey of Label-noise Representation Learning: Past, Present and Future

Reference 15

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

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Learnin g from noisy labels with deep neural networks: A survey,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:29.929625Z

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This paper cites Weakly supervised learning with side information for noisy labeled images,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Weakly supervised learning with side information for noisy labeled images,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:29.782895Z

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Observation ec0f480a-1cc2-4f96-8fb7-bccac1ef03ec · outbound

This paper cites Lea rning with noisy labels via sparse regularization,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Lea rning with noisy labels via sparse regularization,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:29.593995Z

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This paper cites Fine- grained classiﬁcation with noisy labels,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Fine- grained classiﬁcation with noisy labels,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:29.438406Z

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This paper cites Meta label cor rection for noisy label learning,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Meta label cor rection for noisy label learning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:29.263504Z

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This paper cites NoiseBox: To wards more efﬁcient and effective learning with noisy labels,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise NoiseBox: To wards more efﬁcient and effective learning with noisy labels,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:29.060511Z

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This paper cites Robust classiﬁcation from noisy labels: Integrating additional knowledge for chest radiography abnormality as sessment,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Robust classiﬁcation from noisy labels: Integrating additional knowledge for chest radiography abnormality as sessment,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:28.865600Z

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This paper cites Improving medical images classiﬁcation with label noise using dual-uncertainty estimation,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Improving medical images classiﬁcation with label noise using dual-uncertainty estimation,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:28.693191Z

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Observation e29ec5fc-a1b9-4c5a-b6ae-c05cf7c82825 · outbound

This paper cites A fundus image classiﬁcation framework for learning with noisy labels,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise A fundus image classiﬁcation framework for learning with noisy labels,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:28.548737Z

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This paper cites Robust stocha stic neural ensemble learning with noisy labels for thoracic disease cl assiﬁcation,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Robust stocha stic neural ensemble learning with noisy labels for thoracic disease cl assiﬁcation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:28.290306Z

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This paper cites Federated optimization in heterogeneous networks ,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Federated optimization in heterogeneous networks ,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:28.123173Z

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This paper cites Robust federated learning: The case of afﬁne distribution shifts,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Robust federated learning: The case of afﬁne distribution shifts,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:27.869115Z

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This paper cites Fed-DR-Filte r: Using global data representation to reduce the impact of noisy lab els on the performance of federated learning,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Fed-DR-Filte r: Using global data representation to reduce the impact of noisy lab els on the performance of federated learning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:27.664371Z

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

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Fedcorr: Multi- stage federated learning for label noise correction,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:27.496786Z

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This paper cites Robust federated learning with noisy and heterogeneous clients,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Robust federated learning with noisy and heterogeneous clients,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:27.389677Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f6c04197-f46c-4daa-a3db-5a083cb5fa38 · outbound

This paper cites Towards federated learning against noisy labels via local self-regularization,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Towards federated learning against noisy labels via local self-regularization,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:27.181468Z

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This paper cites Curriculum- Based Federated Learning for Machine Fault Diagnosis With Noisy L abels,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Curriculum- Based Federated Learning for Machine Fault Diagnosis With Noisy L abels,

Reference 32

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Observation c038eb6b-6f51-4ba5-9d1f-f86b9e8c0907 · outbound

This paper cites Federated data quality assessment approach: robust learn ing with mixed label noise,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Federated data quality assessment approach: robust learn ing with mixed label noise,

Reference 33

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This paper cites FedNoisy: Federated Noisy Label Learning Benchmark.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise FedNoisy: Federated Noisy Label Learning Benchmark

Reference 34

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

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels

Reference 35

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This paper cites Medical federated l earning with joint graph puriﬁcation for noisy label learning,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Medical federated l earning with joint graph puriﬁcation for noisy label learning,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:26.840768Z

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Observation 7d81b3dd-e3e7-4896-a9c5-8243d7ad1dcd · outbound

This paper cites Intelligent hand ling of noise in federated learning with co-training for enhanced diagnost ic precision,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Intelligent hand ling of noise in federated learning with co-training for enhanced diagnost ic precision,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:26.650405Z

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Observation 52bac696-a3f4-4bfe-8f5b-ca18e3790f76 · outbound

This paper cites Improving speaker veriﬁ cation with noise-aware label ensembling and sample selection: Le arning and correcting noisy speaker labels,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Improving speaker veriﬁ cation with noise-aware label ensembling and sample selection: Le arning and correcting noisy speaker labels,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:26.467273Z

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This paper cites Permuter, J.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Permuter, J

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:26.319536Z

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This paper cites SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning

Reference 40

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FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Unresolved cited work

Reference 41

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This paper cites Lienen, C.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Lienen, C

Reference 42

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Observation ecba41d0-8570-4d60-b0ce-2f7f089d4697 · outbound

This paper cites Lienen and E.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Lienen and E

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:25.826408Z

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Observation fe9c8e6c-9f73-46a6-b8f1-eca26d506cb8 · outbound

This paper cites CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks

Reference 44

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Observation b7bc9ed8-1044-4a23-8e89-21f2e2d32367 · outbound

This paper cites Possibility theory, probabili ty theory and multiple-valued logics: A clariﬁcation,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Possibility theory, probabili ty theory and multiple-valued logics: A clariﬁcation,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:25.687797Z

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Observation b9dcb525-95f0-42c6-a8d8-2be9af299b70 · outbound

This paper cites SSP-RACL: Classification of Noisy Fundus Images with Self-Supervised Pretraining and Robust Adaptive Credal Loss.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise SSP-RACL: Classification of Noisy Fundus Images with Self-Supervised Pretraining and Robust Adaptive Credal Loss

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:58:23.059205Z

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Observation 420f992c-74be-4b84-85fa-85e98455903d · outbound

This paper cites Credal Learning Theory.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Credal Learning Theory

Reference 47

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Observation 115a614c-e6db-4ff0-9f9f-6e0ddc12b6ef · outbound

This paper cites Kvasir-Capsule, a video capsule endoscopy dataset,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Kvasir-Capsule, a video capsule endoscopy dataset,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:25.447013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 33678951-cc0d-4417-9bd9-6d8effbdbb8d · outbound

This paper cites A benchmark of oc ular disease intelligent recognition: One shot for multi-disea se detection,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise A benchmark of oc ular disease intelligent recognition: One shot for multi-disea se detection,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:25.192187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5ad8d9c7-fd6f-4fb8-8e9e-fd55ef428285 · outbound

This paper cites Hard sample aware noise robust learning for histopathology image class iﬁcation,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Hard sample aware noise robust learning for histopathology image class iﬁcation,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T17:58:22.219928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation baee7072-21b6-4a49-8465-f57d9e4b3535 · outbound

This paper cites L VM-Med: Learning large-scale self-supervised vision models for medical imaging via second-order graph ma tching,.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise L VM-Med: Learning large-scale self-supervised vision models for medical imaging via second-order graph ma tching,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:24.921425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 985e4c8d-6821-47d9-9816-8911ffc7e8d5 · outbound

This paper cites Ji, et al.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Ji, et al

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:58:24.641595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f5408f91-bd3a-4322-99c2-919299ca0298 · outbound

This paper cites FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity.

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T17:58:22.560816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers