Paper Citation Record · LEDGER

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning

As of 12 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2412.04521.

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

pith.paper-citation-record.v1
2412.04521 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T21:50:58.149123Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

verified exact0
verified fuzzy27
unresolved18
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9f623482-b946-480f-8037-79c856d267ea · outbound

This paper cites Challenges and future directions of secure federated learning: A survey,.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Challenges and future directions of secure federated learning: A survey,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:58.813665Z

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Observation df1324db-2c2a-444e-8092-a8bfa1a00589 · outbound

This paper cites Advances and open problems in federated learning,.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Advances and open problems in federated learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:58.799088Z

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Observation 969b621c-bfdb-4079-a2fc-1e300115fee0 · outbound

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

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Communication-efficient learning of deep networks from decentralized data,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:58.771232Z

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Observation 545c6e29-4150-4a81-8065-be551b25d551 · outbound

This paper cites Exploring Vacant Classes in Label-Skewed Federated Learning.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Exploring Vacant Classes in Label-Skewed Federated Learning

Reference 5

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Observation 92c74e55-32a7-4a20-af38-9a1ed9b65b0d · outbound

This paper cites Federated optimization in heterogeneous networks,.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Federated optimization in heterogeneous networks,

Reference 6

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Observation a2eb0be9-b4f0-426b-bc64-015d9671f051 · outbound

This paper cites Heterogeneous feder- ated learning: State-of-the-art and research challenges,.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Heterogeneous feder- ated learning: State-of-the-art and research challenges,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:58.785280Z

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Observation f05315b8-0a9a-4db8-93a9-d60609255271 · outbound

This paper cites Federated Learning with Non-IID Data.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Federated Learning with Non-IID Data

Reference 8

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Observation bb87fcde-218a-429d-be0c-401fdbc7ae51 · outbound

This paper cites CLIP-guided Federated Learning on Heterogeneous and Long-Tailed Data.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning CLIP-guided Federated Learning on Heterogeneous and Long-Tailed Data

Reference 9

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Observation b70d4a0f-d536-480d-a493-898945d275e3 · outbound

This paper cites Decoupling Representation and Classifier for Long-Tailed Recognition.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Decoupling Representation and Classifier for Long-Tailed Recognition

Reference 10

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Observation a0b2edc0-5589-4e8d-8643-a56511fb4491 · outbound

This paper cites FedGH: Heterogeneous federated learning with generalized global header,.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning FedGH: Heterogeneous federated learning with generalized global header,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:58.742167Z

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Observation f49bd388-5f66-426b-b7b6-81164d837477 · outbound

This paper cites FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning,.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:58.725448Z

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Observation 92679634-7e52-478a-94e0-561bddbfd74e · outbound

This paper cites Fedx: Unsupervised federated learning with cross knowledge distillation,.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Fedx: Unsupervised federated learning with cross knowledge distillation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:58.710804Z

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Observation 9f8f9a42-ef7d-4e66-9b9f-97797fae5803 · outbound

This paper cites Fedproto: Federated prototype learning across heterogeneous clients,.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Fedproto: Federated prototype learning across heterogeneous clients,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:58.696252Z

Source-reported events for the cited work

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Observation e2cfe264-8969-42fa-a260-7f9df5dc6f3c · outbound

This paper cites PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees,.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:58.681324Z

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Observation 8619c319-15fd-468e-b822-3442a8d0a721 · outbound

This paper cites MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis

Reference 16

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Observation a2db0012-6ecd-4e4d-b8b8-f4e16ecb5df8 · outbound

This paper cites Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data

Reference 17

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Observation 0936c415-8af6-4add-9842-c6202c4d5919 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Distilling the Knowledge in a Neural Network

Reference 18

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Observation dd5c4f7b-b77c-4913-bd27-c310b58b1eb7 · outbound

This paper cites Federated learning based on dynamic regularization,.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Federated learning based on dynamic regularization,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:58.666270Z

Source-reported events for the cited work

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Observation 3c1c1747-b06a-4047-bd53-04db59cee6c7 · outbound

This paper cites Model-contrastive federated learning,.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Model-contrastive federated learning,

Reference 20

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Observation 6beed1b9-b423-4a4a-a9a3-a5b175815e07 · outbound

This paper cites Deep leakage from gradients,.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Deep leakage from gradients,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:58.641731Z

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Observation 2078f1f0-021d-4ae4-80b3-b5fb1b651bbd · outbound

This paper cites Momentum contrast for unsupervised visual representation learning,.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Momentum contrast for unsupervised visual representation learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:58.627345Z

Source-reported events for the cited work

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Observation 739c4e22-621a-4f72-8f42-92216225ec04 · outbound

This paper cites Debiased contrastive learning for sequential recommendation,.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Debiased contrastive learning for sequential recommendation,

Reference 23

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Observation bf2287aa-9f7f-41f8-b0af-a0f802108546 · outbound

This paper cites CPCL: Cross-Modal Prototypical Contrastive Learning for Weakly Supervised Text-based Person Retrieval.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning CPCL: Cross-Modal Prototypical Contrastive Learning for Weakly Supervised Text-based Person Retrieval

Reference 24

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This paper cites A simple framework for contrastive learning of visual representations,.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning A simple framework for contrastive learning of visual representations,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:58.597846Z

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Observation f9464be4-6d4f-4cc4-a972-0d85cd412198 · outbound

This paper cites An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning,.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:58.582343Z

Source-reported events for the cited work

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Observation e6530f72-d07d-4f99-96e0-523a722a5e65 · outbound

This paper cites Fair federated learning under domain skew with local consistency and domain diversity,.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Fair federated learning under domain skew with local consistency and domain diversity,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:58.567181Z

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Observation 90db3224-04e4-4a93-bce0-1c6f97d39c77 · outbound

This paper cites Dafkd: Domain-aware federated knowledge distillation,.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Dafkd: Domain-aware federated knowledge distillation,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:58.551117Z

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Observation 8048729f-297a-41c4-a193-4973600c4f37 · outbound

This paper cites Fedaux: Leveraging unlabeled auxiliary data in federated learning,.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Fedaux: Leveraging unlabeled auxiliary data in federated learning,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:58.536014Z

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Observation 82fac9cb-6e8d-4180-965d-74c15037dbdf · outbound

This paper cites Exploiting shared representations for personalized federated learning,.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Exploiting shared representations for personalized federated learning,

Reference 30

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Observation 4810b17f-4282-4fd0-8da2-31f4c6e6f1c4 · outbound

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FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Fedala: Adaptive local aggregation for personalized federated learning,

Reference 31

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This paper cites Federated Learning with Personalization Layers.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Federated Learning with Personalization Layers

Reference 32

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FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Federated recommendation with additive personalization,

Reference 33

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Observation c981d507-2f32-4105-86e9-3a581791e6a1 · outbound

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FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Towards personalized federated learning,

Reference 34

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This paper cites Large Language Models: A Survey.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Large Language Models: A Survey

Reference 35

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FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning A survey on large language models for recommendation,

Reference 36

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Observation 1a0fcb88-7f29-4ae7-b730-83f1ca68b84d · outbound

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FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning A survey on evaluation of large language models,

Reference 37

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FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Deep learning with differential privacy,

Reference 38

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This paper cites FedUV: Uniformity and variance for heterogeneous federated learning,.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning FedUV: Uniformity and variance for heterogeneous federated learning,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:58.420977Z

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Observation 8786042c-ca73-4cf8-8e47-4d18a915d59e · outbound

This paper cites FedBABU: Towards Enhanced Representation for Federated Image Classification.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning FedBABU: Towards Enhanced Representation for Federated Image Classification

Reference 40

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This paper cites Personalized Federated Learning: A Meta-Learning Approach.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Personalized Federated Learning: A Meta-Learning Approach

Reference 41

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FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Deep residual learning for image recognition,

Reference 42

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FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Pytorch: An imperative style, high-performance deep learning library,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:58.396526Z

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Observation 54c57ab3-6f90-4f18-9683-a98f82140b74 · outbound

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FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Adam: A Method for Stochastic Optimization

Reference 44

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This paper cites Shufflenet: An extremely efficient convolutional neural network for mobile devices,.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning Shufflenet: An extremely efficient convolutional neural network for mobile devices,

Reference 45

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This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T21:50:58.149123Z

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Pith citing papers

No inbound Pith citation observations are available.