Paper Citation Record · LEDGER

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization

As of 8 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2509.10521.

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

pith.paper-citation-record.v1
2509.10521 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:44:00.844806Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

19 of 19 outbound references displayed

verified exact1
verified fuzzy9
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6c411bc0-02e6-4c71-a0a1-5f02022fb74a · outbound

This paper cites Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang.

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T10:43:59.416401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8abd5f0b-6837-4262-b6a9-042b802c8641 · outbound

This paper cites Federated Learning with Personalization Layers.

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization Federated Learning with Personalization Layers

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T10:43:59.468876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54294fff-4f58-49d6-ac9e-052544b3afdb · outbound

This paper cites Flower: A Friendly Federated Learning Research Framework.

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization Flower: A Friendly Federated Learning Research Framework

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T10:43:59.548131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e44f3ae-9be4-45f9-8a1d-e9b522491dbe · outbound

This paper cites Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth.

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T10:43:59.621691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6856db60-7cfa-420e-a086-210b461deaf5 · outbound

This paper cites Pfedsim: An efficient federated control method for clustered training.

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization Pfedsim: An efficient federated control method for clustered training

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:44:02.666355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1f8c7937-dc38-404a-9ae9-d4f692286790 · outbound

This paper cites Weinberger.

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization Weinberger

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:44:02.512102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9a9bca51-c95d-4a49-99d8-d06f1c7752e4 · outbound

This paper cites Monitoring dynamics of emotional sentiment in social network commentaries.

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization Monitoring dynamics of emotional sentiment in social network commentaries

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:44:02.373039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 74ff3a97-ff86-4477-8930-728438db4140 · outbound

This paper cites A visual approach to tracking emotional sentiment dynamics in social network commentaries.

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization A visual approach to tracking emotional sentiment dynamics in social network commentaries

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:44:02.229522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 741f8e0a-22e3-4ba1-8a65-ddbf33486bae · outbound

This paper cites Fedrep: Towards horizontal federated load forecasting for retail energy providers.

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization Fedrep: Towards horizontal federated load forecasting for retail energy providers

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:44:02.049412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 23bece41-2413-46a2-ab64-93e87d284252 · outbound

This paper cites Fedpop: A bayesian approach for personalised federated learning.

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization Fedpop: A bayesian approach for personalised federated learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:44:01.898098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0bd02adb-95db-4ee0-ba2b-c8adcda505e7 · outbound

This paper cites Ditto: Fair and Robust Federated Learning Through Personalization.

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization Ditto: Fair and Robust Federated Learning Through Personalization

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:00.164098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9582e354-f514-4f5a-920c-f0ff9284b576 · outbound

This paper cites On the Convergence of FedAvg on Non-IID Data.

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization On the Convergence of FedAvg on Non-IID Data

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:00.239516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f2d81d9-1bcb-4c25-ae5f-713b86b4e5ca · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:00.335396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac325082-dc76-4255-8193-1d86d961b4a1 · outbound

This paper cites Brendan McMahan, E.

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization Brendan McMahan, E

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:44:01.757069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8b819704-dbff-4515-9373-ce57681ae9d7 · outbound

This paper cites Parametric UMAP embeddings for representation and semi-supervised learning.

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization Parametric UMAP embeddings for representation and semi-supervised learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:00.510611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a84344e7-b2f4-4050-8583-ba8e49608037 · outbound

This paper cites Case studies on x-ray imaging, mri and nuclear imaging.

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization Case studies on x-ray imaging, mri and nuclear imaging

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:44:01.619698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8dee5ac6-5a88-405e-9f95-86dd1518f127 · outbound

This paper cites Membership inference attacks against machine learning models.

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization Membership inference attacks against machine learning models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:00.658720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ba84e4c-03cb-44ea-aed6-47c767f68509 · outbound

This paper cites A novel hierarchical federated learning with self-regulated decentralized clustering.

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization A novel hierarchical federated learning with self-regulated decentralized clustering

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:44:01.447227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0d072c16-5b30-4eb7-9fc8-66bf332b893d · outbound

This paper cites Personalized Federated Learning via Variational Bayesian Inference.

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization Personalized Federated Learning via Variational Bayesian Inference

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-05T10:44:01.045980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.