Paper Citation Record · LEDGER

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis

As of 18 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 2 inbound Pith citation observations for arXiv:2412.16083.

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

pith.paper-citation-record.v1
2412.16083 v2

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T10:51:53.121348Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:07:53.079985Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T08:05:31.307708Z

Reference resolution

55 of 55 outbound references displayed

verified exact1
verified fuzzy33
unresolved21
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d054b1ce-6808-493f-9fa5-adab84472289 · outbound

This paper cites Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:54.029021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e757452f-ccca-4af5-becc-c0eff2600e8a · outbound

This paper cites ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:52.884869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38d7b3fe-14c4-4bf5-b774-eba2af52aa47 · outbound

This paper cites On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:54.014687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5e827b15-5e31-4cb7-bd7d-159b7ccfac7a · outbound

This paper cites Advances and Open Problems in Federated Learning.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Advances and Open Problems in Federated Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:52.894847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0088d7fe-30d3-4ec1-a027-d31b6bc7cb6d · outbound

This paper cites Communication-Efficient Learning of Deep Networks from Decentral- ized Data,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Communication-Efficient Learning of Deep Networks from Decentral- ized Data,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:54.001148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation be126ca6-0213-4f9f-bd63-2c9cb43dca0f · outbound

This paper cites Federated Learning: Collaborative Ma- chine Learning Without Centralized Training Data,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Federated Learning: Collaborative Ma- chine Learning Without Centralized Training Data,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.985692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 17d17a01-debc-42ab-b883-8ebf27cf68bd · outbound

This paper cites Our data, ourselves: Privacy via distributed noise generation,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Our data, ourselves: Privacy via distributed noise generation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.970458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c0e5d779-d80c-41e2-be49-c091c47fd94b · outbound

This paper cites Synthetic data generation for fraud detection using gans,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Synthetic data generation for fraud detection using gans,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.956302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1d6ac5dc-fe71-47d5-a965-e7978921a42c · outbound

This paper cites Synthesizing test data for fraud detection systems,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Synthesizing test data for fraud detection systems,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.941783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 090b24e3-4dc4-4206-86e4-8052867b1c94 · outbound

This paper cites FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:52.923142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99a70251-8fd1-4b1e-a73a-8dcc727adbf4 · outbound

This paper cites Diffusion Models Beat GANs on Image Synthesis,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Diffusion Models Beat GANs on Image Synthesis,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.927007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation da6c3107-9589-481b-aaee-879957e34d25 · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis High-Resolution Image Synthesis with Latent Diffusion Models,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.912942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ca4ff5b5-1a2f-431e-8009-3ce6a10d520a · outbound

This paper cites Findiff: Diffusion models for financial tabular data generation,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Findiff: Diffusion models for financial tabular data generation,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:52.936786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7e7261c-1d26-45cc-b0ab-071482c68df1 · outbound

This paper cites A Survey on Generative Diffusion Model.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis A Survey on Generative Diffusion Model

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:52.940983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ab5f303-8389-425a-ab1f-e0974f9e620f · outbound

This paper cites Diffusion Models: A Com- prehensive Survey of Methods and Applications,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Diffusion Models: A Com- prehensive Survey of Methods and Applications,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:52.945695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5fac426-4f6a-4a31-849d-2f18327666d8 · outbound

This paper cites Diffusion Models in Vision: A Survey,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Diffusion Models in Vision: A Survey,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.889838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b94bc677-4a9d-41d5-9cc2-0866e16ab7fa · outbound

This paper cites Federated Learn- ing: A Survey on Enabling Technologies, Protocols, and Applications,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Federated Learn- ing: A Survey on Enabling Technologies, Protocols, and Applications,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.875533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 03b75c31-7053-41ec-8052-2718097915b2 · outbound

This paper cites A survey on federated learning systems: Vision, hype and reality for data privacy and protection,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis A survey on federated learning systems: Vision, hype and reality for data privacy and protection,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:52.958432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4f7b1f1-02d5-4b06-a6cb-833bd059c874 · outbound

This paper cites A Survey on Federated Learning,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis A Survey on Federated Learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.852000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b70ee721-cc03-4e3c-9585-68c2569e4b36 · outbound

This paper cites Modeling tabular data using conditional gan,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Modeling tabular data using conditional gan,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.837943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9ddae2cc-1a5a-4a69-8a30-ab20cb608b3c · outbound

This paper cites Conditional Wasserstein GAN-based oversampling of tabular data for imbalanced learning,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Conditional Wasserstein GAN-based oversampling of tabular data for imbalanced learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.824241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 49bf1418-6dca-4c86-ac33-bdebc74892c6 · outbound

This paper cites PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.809879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1d8f1c6a-c058-46f2-98fa-35b79253e574 · outbound

This paper cites Differentially Private Synthetic Medical Data Generation Using Convolutional GANs,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Differentially Private Synthetic Medical Data Generation Using Convolutional GANs,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.793785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0d1b148a-ca1e-42f0-a734-33b8b5a60683 · outbound

This paper cites Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-11T10:51:53.372764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9c9691de-5783-4c54-9bce-550bb4d8c885 · outbound

This paper cites On the privacy properties of gan- generated samples,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis On the privacy properties of gan- generated samples,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.779487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e84f6eb0-e127-4be9-a55f-af1c30031082 · outbound

This paper cites CTAB-GAN: Effective Table Data Synthesizing,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis CTAB-GAN: Effective Table Data Synthesizing,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.764859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ab14c418-7306-4c11-8ac2-5db43952b661 · outbound

This paper cites Tabddpm: Modelling tabular data with diffusion models,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Tabddpm: Modelling tabular data with diffusion models,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.750799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 63fa607c-aec1-496f-8f68-43023840654c · outbound

This paper cites Argmax flows and multinomial diffusion: Learning categorical distributions,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Argmax flows and multinomial diffusion: Learning categorical distributions,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.736568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c9bea5e1-ad94-4348-b6ef-d8c1d54c9a85 · outbound

This paper cites Imb-findiff: Conditional diffusion models for class imbalance synthesis of financial tabular data,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Imb-findiff: Conditional diffusion models for class imbalance synthesis of financial tabular data,

Reference 29

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Source-reported events for the cited work

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Observation 91a18375-64fd-496b-9535-654dbccd5707 · outbound

This paper cites Frauddiffuse: Diffusion-aided syn- thetic fraud augmentation for improved fraud detection,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Frauddiffuse: Diffusion-aided syn- thetic fraud augmentation for improved fraud detection,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.713435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 948d971f-d032-4953-8af2-61bb37c613a4 · outbound

This paper cites Training Diffusion Models with Federated Learning: A Communication-Efficient Model for Cross-Silo Federated Image Gener- ation,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Training Diffusion Models with Federated Learning: A Communication-Efficient Model for Cross-Silo Federated Image Gener- ation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.699323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 59ea8409-7153-4c64-9496-d881d6497a4d · outbound

This paper cites Phoenix: A Federated Generative Diffusion Model,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Phoenix: A Federated Generative Diffusion Model,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:53.019937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ae3dfa0-98dc-417f-89a3-0c86c22060d8 · outbound

This paper cites Deep learning with differential privacy,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Deep learning with differential privacy,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:53.024328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec1a34e1-34b6-459d-8625-9345985f6f75 · outbound

This paper cites Differentially Private Diffusion Models.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Differentially Private Diffusion Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:53.028753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cfd99aa-b9c6-4a01-adce-8228385ad7f3 · outbound

This paper cites A survey of differentially private generative adversarial net- works,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis A survey of differentially private generative adversarial net- works,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.675742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bb95d681-726f-4398-9783-7a1759d5ab3e · outbound

This paper cites A Systematic Review of Federated Generative Models.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis A Systematic Review of Federated Generative Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:53.037916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9dc7a31c-8889-4841-815f-4a3e48e2922a · outbound

This paper cites Gs-wgan: A gradient-sanitized approach for learning differentially private generators,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Gs-wgan: A gradient-sanitized approach for learning differentially private generators,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.661919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5a884bc0-ebe5-437b-8044-724f786d67ba · outbound

This paper cites Sgde: Secure generative data exchange for cross-silo federated learning,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Sgde: Secure generative data exchange for cross-silo federated learning,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.647103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 02f2bb1c-785a-4ac8-a1a7-b8491becc9ef · outbound

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

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Generative Models for Effective ML on Private, Decentralized Datasets

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:53.050682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a253b6c-9bfb-42d5-b936-988df009c4f2 · outbound

This paper cites Feddpgan: Fed- erated differentially private generative adversarial networks framework for the detection of covid-19 pneumonia,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Feddpgan: Fed- erated differentially private generative adversarial networks framework for the detection of covid-19 pneumonia,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.631957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0d625ac6-9439-4de1-b0b3-b817c01766b9 · outbound

This paper cites Differentially private secure multi- party computation for federated learning in financial applications,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Differentially private secure multi- party computation for federated learning in financial applications,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.617602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4befcd3a-220a-4558-b932-5996e5ca6be4 · outbound

This paper cites Federated and Privacy- Preserving Learning of Accounting Data in Financial Statement Audits,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Federated and Privacy- Preserving Learning of Accounting Data in Financial Statement Audits,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.603589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 899aa4df-55ef-483e-8aaa-2e6f4381d244 · outbound

This paper cites Deep Unsupervised Learning Using Nonequilibrium Thermodynamics,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Deep Unsupervised Learning Using Nonequilibrium Thermodynamics,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.589639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ae980aaa-f541-42ae-8dba-5a2e13d286f0 · outbound

This paper cites Denoising Diffusion Probabilistic Models,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Denoising Diffusion Probabilistic Models,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.574991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 12d10a7e-f0bc-4cfa-bf5c-8a9dde61b93e · outbound

This paper cites The algorithmic foundations of differential privacy,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis The algorithmic foundations of differential privacy,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:53.077190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 037b6250-a59e-4009-b9c8-37cd0d057dd5 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Pytorch: An imperative style, high-performance deep learning library,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:53.081736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05476e70-c22d-4402-807c-ad5ead4d9ea4 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Adam: A Method for Stochastic Optimization

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:53.085910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c978a87-5514-4d2a-b08b-d2831cd70d4c · outbound

This paper cites Flower: A friendly federated learning research framework,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Flower: A friendly federated learning research framework,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.543401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 759ba6ef-d6cc-4e02-a642-cab1e7c12ad4 · outbound

This paper cites Adaptive Federated Optimization.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Adaptive Federated Optimization

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:53.094377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cec3eea-582b-45e2-b9bb-aa34f4dbf3ff · outbound

This paper cites Federated optimization in heterogeneous networks,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Federated optimization in heterogeneous networks,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:53.099084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95870773-418a-427a-b4ac-363901bdac2c · outbound

This paper cites Opacus: User-Friendly Differential Privacy Library in PyTorch.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:53.103437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6fae5cd-1893-4c34-a45b-23d9a754aa9f · outbound

This paper cites A Unified Framework for Quantifying Privacy Risk in Synthetic Data.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis A Unified Framework for Quantifying Privacy Risk in Synthetic Data

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:53.108248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0306ca7-f920-4cc4-8154-1316c5bb0870 · outbound

This paper cites Opinion 05/2014 on anonymisation techniques,.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Opinion 05/2014 on anonymisation techniques,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.520238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 08b81ccf-9554-4b4c-8f61-321dd520eacd · outbound

This paper cites Zychlinski, “dython,” 2018.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis Zychlinski, “dython,” 2018

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:53.506573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fd865952-e0a6-4dad-b638-8d5de5eb2b2e · outbound

This paper cites SCAFFOLD: Stochastic Controlled Averaging for Federated Learning.

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis SCAFFOLD: Stochastic Controlled Averaging for Federated Learning

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:53.121348Z

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Pith citing papers

Observation 4b984779-3f84-4ff3-9a6e-a81a5d1f8cae · inbound

Diffusion and Flow Matching Models for Tabular Data: A Survey cites this paper.

Diffusion and Flow Matching Models for Tabular Data: A Survey Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis

Reference 126

Resolution: verified exact; arxiv_id, observed 2026-05-25T08:05:31.309838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7ed8d17f-0eea-495e-a90d-84ce07eeff89 · inbound

Diffusion Models in Finance: A Survey cites this paper.

Diffusion Models in Finance: A Survey Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-16T00:07:53.079985Z

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