Paper Citation Record · LEDGER

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2411.12377.

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

pith.paper-citation-record.v1
2411.12377 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:52:02.256519Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T21:58:58.243453Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e0a6f746-fa38-438a-980b-7a5cdbc77ed7 · inbound

FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation cites this paper.

FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-19T08:12:10.720018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c7625bcf-85a5-41f0-86d2-8cd18dd50912 · inbound

Model Fusion via Retrofitting cites this paper.

Model Fusion via Retrofitting Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T23:52:02.256519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce364021-0b8a-4cf9-b95c-ec525c77f02c · inbound

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks cites this paper.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T11:57:34.165445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27ec88a5-efc3-4959-af7d-8132f654bdaf · inbound

Task2vec Readiness: Diagnostics for Federated Learning from Pre-Training Embeddings cites this paper.

Task2vec Readiness: Diagnostics for Federated Learning from Pre-Training Embeddings Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T10:51:03.018242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 36e5ebd7-98e7-4df0-baec-c4e7177277b0 · inbound

When More Parameters Hurt: Foundation Model Priors Amplify Worst-Client Disparity Under Extreme Federated Heterogeneity cites this paper.

When More Parameters Hurt: Foundation Model Priors Amplify Worst-Client Disparity Under Extreme Federated Heterogeneity Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T02:41:17.228267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7f5d33ee-1fd9-4bc9-9a7a-cddde85d37bd · inbound

BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregation cites this paper.

BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregation Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-20T14:28:21.185575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 983792d9-7157-4f4a-9186-08603eb461b0 · inbound

AlignFed: Alignment-Aware Asynchronous Federated Fine-Tuning for Large Language Models in Heterogeneous Edge Environments cites this paper.

AlignFed: Alignment-Aware Asynchronous Federated Fine-Tuning for Large Language Models in Heterogeneous Edge Environments Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T21:17:24.956943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d2b9acd5-96a3-4388-82f9-1243cf3fbb3d · inbound

Entropy-Regularized Probabilistic Gates for Sparse Model Discovery in Scarce-Data Federated Learning cites this paper.

Entropy-Regularized Probabilistic Gates for Sparse Model Discovery in Scarce-Data Federated Learning Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-02T19:27:18.633389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation eab8db7e-eb97-47f4-9859-17932070a184 · inbound

Benchmarking Federated Learning and Knowledge Distillation for Point Cloud Classification cites this paper.

Benchmarking Federated Learning and Knowledge Distillation for Point Cloud Classification Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-03T21:58:58.245623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 916dc3ef-bbf1-40f8-a953-0d1cf145ddf8 · inbound

PIcsC: Partitioning-Induced Covariate Shift Correction cites this paper.

PIcsC: Partitioning-Induced Covariate Shift Correction Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T02:30:36.253345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 606089d8-7a43-491e-91c5-326c009cd949 · inbound

On the Effectiveness of Adaptation Strategies for VLM-Based Federated Learning in Remote Sensing cites this paper.

On the Effectiveness of Adaptation Strategies for VLM-Based Federated Learning in Remote Sensing Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:05.008101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.