Paper Citation Record · LEDGER

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy

As of 17 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2604.22562.

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

pith.paper-citation-record.v1
2604.22562 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-08T12:26:38.542442Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-09T08:11:23.216344Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T08:16:05.695995Z

Reference resolution

25 of 25 outbound references displayed

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External citation measurements

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Outbound references

Observation 81d5de83-ea41-4632-a70f-7e7183286042 · outbound

This paper cites Com- putation of the von neumann entropy of large matrices via trace estimators and rational krylov methods.Numerische Mathematik, 155(3):377–414.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Com- putation of the von neumann entropy of large matrices via trace estimators and rational krylov methods.Numerische Mathematik, 155(3):377–414

Reference 1

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Observation b421009e-6c48-437d-9b78-f8ea46bfd586 · outbound

This paper cites LEAF: A Benchmark for Federated Settings.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy LEAF: A Benchmark for Federated Settings

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:16:08.231877Z

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Observation 2b9b19f8-cc64-4f46-8293-f4a1afd657b0 · outbound

This paper cites Fair federated medical image segmentation via client contri- bution estimation.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Fair federated medical image segmentation via client contri- bution estimation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:22:24.663081Z

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Observation 3bdff941-70be-424a-a183-ae994ce14e36 · outbound

This paper cites Fantastic generalization mea- sures and where to find them.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Fantastic generalization mea- sures and where to find them

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:22:24.658256Z

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Observation 1ded3145-b232-44bc-815a-a70e634c15b6 · outbound

This paper cites Advances and open problems in federated learning.Foundations and trends in machine learning, 14(1-2):1–210.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Advances and open problems in federated learning.Foundations and trends in machine learning, 14(1-2):1–210

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:22:24.655850Z

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Observation 75c2df85-c833-4b45-8a83-cff98ce68632 · outbound

This paper cites A new approach to linear filtering and prediction problems.Transactions of the ASME–Journal of Basic Engineering, 82(Series D):35–45.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy A new approach to linear filtering and prediction problems.Transactions of the ASME–Journal of Basic Engineering, 82(Series D):35–45

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:22:24.660863Z

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Observation 613e6ecf-feb9-4c75-aad2-87fe894613c2 · outbound

This paper cites Data valuation and detections in federated learning.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Data valuation and detections in federated learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:22:24.702068Z

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Observation c29236e2-6f2f-4993-8fad-69b20e1029aa · outbound

This paper cites Collabo- rative fairness in federated learning.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Collabo- rative fairness in federated learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:22:24.699994Z

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Observation 92433b6f-9e90-42cb-aa4d-327e7327194a · outbound

This paper cites Guiding neural collapse: Optimising towards the nearest simplex equiangular tight frame.Advances in Neural Information Processing Systems, 37:35544–35573.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Guiding neural collapse: Optimising towards the nearest simplex equiangular tight frame.Advances in Neural Information Processing Systems, 37:35544–35573

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:22:24.693349Z

Source-reported events for the cited work

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Observation 8aee3fe5-224f-4926-8465-99b5f45f153f · outbound

This paper cites Implicit self- regularization in deep neural networks: Evidence from ran- dom matrix theory and implications for learning.Journal of Machine Learning Research, 22(165):1–73.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Implicit self- regularization in deep neural networks: Evidence from ran- dom matrix theory and implications for learning.Journal of Machine Learning Research, 22(165):1–73

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:22:24.695670Z

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Observation d162e911-60be-4dbf-9da9-ba699135a9a3 · outbound

This paper cites Martin, Tongsu, Peng, and Michael W.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Martin, Tongsu, Peng, and Michael W

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:22:24.691048Z

Source-reported events for the cited work

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Observation 08ea9f90-9874-4b99-8ade-c0fcc475336c · outbound

This paper cites Communication- Efficient Learning of Deep Networks from Decentralized Data.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Communication- Efficient Learning of Deep Networks from Decentralized Data

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:22:24.697864Z

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Observation 616ef7dc-b415-44f7-a7ee-e03333d0e6f4 · outbound

This paper cites FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings.Advances in Neural Information Pro- cessing Systems, 35:5315–5334.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings.Advances in Neural Information Pro- cessing Systems, 35:5315–5334

Reference 13

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Observation b4f749bf-f7b2-40ef-884f-a77c6354b2ab · outbound

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Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Unresolved cited work

Reference 14

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Observation 0894abb3-8179-4b83-81a5-ae1df6cc6f59 · outbound

This paper cites Springer.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Springer

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:22:24.679913Z

Source-reported events for the cited work

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Observation 047693e7-63e7-448c-bb8f-343f5e1fd26c · outbound

This paper cites Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Kone ˇcn´y, Sanjiv Kumar, and Hugh Brendan McMahan.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Kone ˇcn´y, Sanjiv Kumar, and Hugh Brendan McMahan

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:22:24.684207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f60471ce-91fd-4850-9b30-663d2c4d6a53 · outbound

This paper cites Redefining Contribu- tions: Shapley-Driven Federated Learning.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Redefining Contribu- tions: Shapley-Driven Federated Learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:22:24.686434Z

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Observation 12f2cdb3-56b0-480c-97fe-e92e957e48b4 · outbound

This paper cites 1, 2, 3, 5, 6.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy 1, 2, 3, 5, 6

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:22:24.675933Z

Source-reported events for the cited work

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Observation 248525bd-64b5-4669-8d2c-786b1984c872 · outbound

This paper cites Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:22:24.677933Z

Source-reported events for the cited work

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Observation 4c8f6780-5743-4a90-b9d3-36c240f1bb3f · outbound

This paper cites CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning.Transac- tions on Machine Learning Research.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning.Transac- tions on Machine Learning Research

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:22:24.670887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 843d8c67-636a-4f3c-a637-d9fd44f05f27 · outbound

This paper cites FedPCA: Noise-Robust Fair Federated Learning via Performance-Capacity Analysis.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy FedPCA: Noise-Robust Fair Federated Learning via Performance-Capacity Analysis

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:16:08.224881Z

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Observation b7dea638-1107-4c1a-a339-e6f37a10d87b · outbound

This paper cites Gradient Driven Rewards to Guarantee Fairness in Collaborative Ma- chine Learning.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Gradient Driven Rewards to Guarantee Fairness in Collaborative Ma- chine Learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:22:24.673712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5e128efa-2610-4257-a145-7cd1743ef22c · outbound

This paper cites A new perspective to boost performance fairness for medical federated learning.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy A new perspective to boost performance fairness for medical federated learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:22:24.668349Z

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Observation da764fcd-2a5f-4903-a329-061a61dbe15c · outbound

This paper cites Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:16:08.217087Z

Source-reported events for the cited work

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Observation 0583bca5-dd6d-47e4-a16b-d1264ccf8c95 · outbound

This paper cites A Geometric Analysis of Neural Collapse with Unconstrained Features.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy A Geometric Analysis of Neural Collapse with Unconstrained Features

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:22:24.665550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation bddb2bdd-85b4-4eae-9e42-23b8bb37d8c1 · inbound

FedMark-FM: Auditable, Risk-Adjusted Data Markets for Federated Foundation-Model Adaptation cites this paper.

FedMark-FM: Auditable, Risk-Adjusted Data Markets for Federated Foundation-Model Adaptation Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-07-09T08:16:05.698302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.