Paper Citation Record · LEDGER

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling

As of 22 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2504.13462.

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

pith.paper-citation-record.v1
2504.13462 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:11:26.623601Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

23 of 23 outbound references displayed

verified exact0
verified fuzzy12
unresolved11
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 31cdb0b4-b79f-4199-b3f5-5901b0e2e891 · outbound

This paper cites The cost of training machine learning models over distributed data sources,.

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling The cost of training machine learning models over distributed data sources,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:11:26.881394Z

Source-reported events for the cited work

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Observation fb7c7d76-d905-4d96-9370-6c2f9bec0e9a · outbound

This paper cites Federated learning with hierarchical clustering of local updates to improve training on non-IID data,.

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling Federated learning with hierarchical clustering of local updates to improve training on non-IID data,

Reference 2

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Source-reported events for the cited work

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Observation a94f0014-debd-45e4-941a-e9a6da1bc350 · outbound

This paper cites Federated learning under distributed concept drift,.

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling Federated learning under distributed concept drift,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:11:26.860565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5c6918a0-0b76-4f5b-b0e9-7502221c8dfe · outbound

This paper cites Efficient distribution similarity identification in clustered federated learning via principal angles between client data subspaces,.

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling Efficient distribution similarity identification in clustered federated learning via principal angles between client data subspaces,

Reference 4

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Source-reported events for the cited work

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Observation cb144cb1-c88d-412d-98bb-18af7bb163f5 · outbound

This paper cites Clustered federated multi-task learning with non-IID data,.

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling Clustered federated multi-task learning with non-IID data,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:11:26.842085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 76a8cc6e-5d54-4dc6-833c-8d83d2bd6e71 · outbound

This paper cites Per- sonalized cross-silo federated learning on non-iid data,.

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling Per- sonalized cross-silo federated learning on non-iid data,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T12:11:26.556000Z

Source-reported events for the cited work

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Observation 4ef8fc40-bb34-4bd4-afc6-ff9d608bd273 · outbound

This paper cites Adapt to adaptation: Learning personalization for cross-silo federated learning,.

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling Adapt to adaptation: Learning personalization for cross-silo federated learning,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T12:11:26.560899Z

Source-reported events for the cited work

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Observation b56d0437-72ca-42cd-9b33-58d92bfc27ee · outbound

This paper cites FRAug: Tackling federated learning with Non-IID features via representation augmentation,.

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling FRAug: Tackling federated learning with Non-IID features via representation augmentation,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:11:26.817066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b13f0aa9-15ab-443b-93bc-6349c13d4e76 · outbound

This paper cites Feature matching data synthesis for non-iid federated learning,.

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling Feature matching data synthesis for non-iid federated learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:11:26.804522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 77208d47-2c30-4000-a52f-ed18d93cbcfc · outbound

This paper cites Federated optimization in heterogeneous networks,.

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling Federated optimization in heterogeneous networks,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T12:11:26.572928Z

Source-reported events for the cited work

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Observation 7d6359f3-665b-47d4-9aac-61bd8ed53ad4 · outbound

This paper cites Coordinating momenta for cross-silo federated learning,.

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling Coordinating momenta for cross-silo federated learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:11:26.783908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 88cbb59e-2f23-4e62-be94-1c9eb6070a55 · outbound

This paper cites Scaffold: Stochastic controlled averaging for federated learn- ing,.

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling Scaffold: Stochastic controlled averaging for federated learn- ing,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T12:11:26.580584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a8804a3-2ff9-489e-b9ff-15d3d0304759 · outbound

This paper cites Ensemble federated learning with non-iid data in wireless networks,.

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling Ensemble federated learning with non-iid data in wireless networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:11:26.763673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a5cf9278-fb6c-48ab-99fe-9c3e3731b303 · outbound

This paper cites Fed-ensemble: Ensemble models in federated learning for improved generalization and uncertainty quantification,.

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling Fed-ensemble: Ensemble models in federated learning for improved generalization and uncertainty quantification,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:11:26.751317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 16728b18-a140-4f44-8d95-b981aa00c25a · outbound

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

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling Communication-efficient learning of deep networks from decentralized data,

Reference 15

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Source-reported events for the cited work

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Observation 4f8f6713-a0d7-4f16-ac68-c3d7ff2ba02a · outbound

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Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-16T12:11:26.732230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3684c5fe-dd5b-4076-9a12-e7edf4563d2a · outbound

This paper cites an unresolved cited work.

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling Unresolved cited work

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T12:11:26.599623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b6be35d-5446-4111-9c58-4bd9bf780fd1 · outbound

This paper cites Optimizing the collaboration structure in cross-silo federated learning,.

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling Optimizing the collaboration structure in cross-silo federated learning,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T12:11:26.603769Z

Source-reported events for the cited work

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Observation 46ef1280-0894-41b2-bba1-a9ed75572eeb · outbound

This paper cites Clustered sampling: Low-variance and improved representativity for clients selection in federated learning,.

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling Clustered sampling: Low-variance and improved representativity for clients selection in federated learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:11:26.705351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a09be62c-95a4-4e32-8648-6befc00cd993 · outbound

This paper cites Fedsts: A stratified client selection framework for consistently fast federated learning,.

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling Fedsts: A stratified client selection framework for consistently fast federated learning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:11:26.693339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation aa757b5a-2563-4acf-ae5d-295e2279b057 · outbound

This paper cites Convergence analysis of sequential federated learning on heterogeneous data,.

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling Convergence analysis of sequential federated learning on heterogeneous data,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:11:26.679072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e94739a5-a234-42cf-b872-674bf212d19e · outbound

This paper cites Why batch normalization damage federated learning on non-iid data?.

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling Why batch normalization damage federated learning on non-iid data?

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:11:26.666204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f4994f2b-355c-44bd-9f6a-71fa2dc7910f · outbound

This paper cites Federated learning on non-iid data silos: An experimental study,.

Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling Federated learning on non-iid data silos: An experimental study,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T12:11:26.623601Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.