Paper Citation Record · LEDGER

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data

As of 20 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2505.09733.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.09733 v1

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measured 41 of 41 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Outbound references

Observation 452a8be4-ab3d-4728-a8da-f0f2b638703d · outbound

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

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Communication-efficient learning of deep networks from decentralized data,

Reference 1

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This paper cites Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture,.

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture,

Reference 2

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This paper cites Efficient and secure federated learning for financial applications,.

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Efficient and secure federated learning for financial applications,

Reference 3

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This paper cites Federated learning in mobile edge computing: An edge-learning perspective for beyond 5g,.

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Federated learning in mobile edge computing: An edge-learning perspective for beyond 5g,

Reference 4

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This paper cites Privacy-preserving real-time action detection in intelligent vehicles using federated learning-based temporal recurrent network,.

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Privacy-preserving real-time action detection in intelligent vehicles using federated learning-based temporal recurrent network,

Reference 5

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This paper cites A survey of federated learning for edge computing: Research problems and solutions,.

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data A survey of federated learning for edge computing: Research problems and solutions,

Reference 6

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

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Federated learning: Challenges, methods, and future directions,

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Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Issues in federated learning: some experiments and preliminary results,

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Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Mnist handwritten digit database,

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This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 10

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This paper cites Federated Learning with Non-IID Data.

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Federated Learning with Non-IID Data

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This paper cites Generative Models for Effective ML on Private, Decentralized Datasets.

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Generative Models for Effective ML on Private, Decentralized Datasets

Reference 12

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This paper cites Robust federated learning with noisy labels,.

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Robust federated learning with noisy labels,

Reference 13

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This paper cites Fedcg: Leverage conditional gan for protecting privacy and maintaining competitive performance in federated learning,.

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Fedcg: Leverage conditional gan for protecting privacy and maintaining competitive performance in federated learning,

Reference 14

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This paper cites Fedar+: A federated learning approach to appliance recognition with mislabeled data in residential environments,.

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Fedar+: A federated learning approach to appliance recognition with mislabeled data in residential environments,

Reference 15

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

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Learning cautiously in federated learning with noisy and heterogeneous clients,

Reference 16

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

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Fednoro: towards noise-robust federated learning by addressing class imbalance and label noise heterogeneity,

Reference 17

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This paper cites FedNoisy: Federated Noisy Label Learning Benchmark.

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data FedNoisy: Federated Noisy Label Learning Benchmark

Reference 18

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Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Collaboratively learning federated models from noisy decentralized data,

Reference 19

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Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Federated learning client pruning for noisy labels,

Reference 20

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This paper cites Federated learning for automatic modulation classification under class imbalance and varying noise condition,.

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Federated learning for automatic modulation classification under class imbalance and varying noise condition,

Reference 21

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Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Robust federated learning with noisy and hetero- geneous clients,

Reference 22

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Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Federated semi- supervised learning with inter-client consistency & disjoint learning,

Reference 23

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Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Federated learning with client-exclusive classes,

Reference 24

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Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Federated Optimization in Heterogeneous Networks

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Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Review of deep learning: concepts, cnn architectures, challenges, applications, future directions,

Reference 26

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Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data A study of cross-validation and bootstrap for accuracy estimation and model selection,

Reference 27

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Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Knowledge Entropy Decay during Language Model Pretraining Hinders New Knowledge Acquisition

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Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Some methods for classification and analysis of multivariate observations,

Reference 29

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Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Least squares quantization in pcm,

Reference 30

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Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Conditional generative adversarial nets,

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Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Gradient-based learning applied to document recognition,

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Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Dropout: A simple way to prevent neural networks from overfit- ting,

Reference 33

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Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

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Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Unsupervised representation learning with deep convolutional generative adversarial networks,

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Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Evaluating the suitability of inception score and fr ´echet inception distance as metrics for quality and diversity in image generation,

Reference 36

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Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Improved techniques for training gans,

Reference 37

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Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Conditional Generative Adversarial Nets

Reference 2014

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Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 2016

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This paper cites Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G.

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G

Reference 2020

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This paper cites Available: https://arxiv.org/abs/2303.08355.

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Available: https://arxiv.org/abs/2303.08355

Reference 2023

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