Paper Citation Record · LEDGER

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data

As of 15 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2505.20485.

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

pith.paper-citation-record.v1
2505.20485 v3

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:59:37.558240Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T16:34:00.285002Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

51 of 51 outbound references displayed

verified exact1
verified fuzzy27
unresolved23
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cac5a2d5-0cf6-4f7c-9634-0c96c30bb052 · outbound

This paper cites Federated Learning Based on Dynamic Regularization.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Federated Learning Based on Dynamic Regularization

Reference 1

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Observation 9457d201-6acf-4671-93ea-3f1ab7fa42b3 · outbound

This paper cites A large annotated corpus for learning natural language inference.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data A large annotated corpus for learning natural language inference

Reference 2

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Observation 50ab88fe-ebcb-4daf-ae5c-d975b6ab1ff8 · outbound

This paper cites On the convergence of decentralized federated learning under imperfect information sharing.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data On the convergence of decentralized federated learning under imperfect information sharing

Reference 3

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Observation 317de3dc-92b0-45da-9ef2-e31941edb084 · outbound

This paper cites Brinton, Stanislaw H Zak, and Zi- ran Wang.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Brinton, Stanislaw H Zak, and Zi- ran Wang

Reference 4

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Observation c5d2cf0c-1a84-4aac-b0f3-1aa7852893b0 · outbound

This paper cites A Survey of Federated Learning for Connected and Automated Vehicles.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data A Survey of Federated Learning for Connected and Automated Vehicles

Reference 5

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Observation c33538eb-4ecb-46f5-9a8e-c4ff2585f200 · outbound

This paper cites FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis

Reference 6

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Observation 978f7bb1-de79-4757-8ea0-334a7457815c · outbound

This paper cites Fedgems: Federated learning of larger server models via selective knowledge fusion.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Fedgems: Federated learning of larger server models via selective knowledge fusion

Reference 7

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Observation e0b50cb5-274e-43e7-8a67-0e34baf24910 · outbound

This paper cites Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning

Reference 8

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Observation 448b0324-f9e2-4ce7-af21-7e483e91b41c · outbound

This paper cites Exploiting shared representations for personalized federated learning.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Exploiting shared representations for personalized federated learning

Reference 9

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Observation b8035556-628d-4be1-8936-a51d5e9fc11d · outbound

This paper cites CINIC-10 is not ImageNet or CIFAR-10.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data CINIC-10 is not ImageNet or CIFAR-10

Reference 10

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Observation 62c68f9f-c8eb-4653-8a28-facd5209c654 · outbound

This paper cites Imagenet: A large- scale hierarchical image database.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Imagenet: A large- scale hierarchical image database

Reference 11

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Observation cba6f21b-ea04-4aec-81a5-4ab34c197215 · outbound

This paper cites Orthogonal gradient descent for continual learning.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Orthogonal gradient descent for continual learning

Reference 12

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Observation e1fd35af-e470-46dd-b4a4-e45a30706d40 · outbound

This paper cites Sharp bounds for federated averaging (local sgd) and continuous perspective.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Sharp bounds for federated averaging (local sgd) and continuous perspective

Reference 13

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Observation 08f58f76-ceda-4e9b-9702-0be807c300c4 · outbound

This paper cites Twitter sentiment classification using distant super- vision.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Twitter sentiment classification using distant super- vision

Reference 14

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Observation fea23de7-c818-4d6b-9978-a1e1f1a61cfb · outbound

This paper cites Preserving privacy in federated learning with ensemble cross-domain knowledge distillation.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Preserving privacy in federated learning with ensemble cross-domain knowledge distillation

Reference 15

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Observation 5b9e2017-6370-406e-bd0d-d5d93a257c36 · outbound

This paper cites On calibration of modern neural networks.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data On calibration of modern neural networks

Reference 16

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Observation b1361187-aa70-491e-8b64-06973978318e · outbound

This paper cites Group knowledge transfer: Fed- erated learning of large cnns at the edge.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Group knowledge transfer: Fed- erated learning of large cnns at the edge

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:41.490765Z

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Observation c40d31ab-a174-490d-9d57-0f81f56d7194 · outbound

This paper cites Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

Reference 18

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Observation 413efbf6-1e51-488b-8506-895164dbe1c2 · outbound

This paper cites TinyBERT: Distilling BERT for Natural Language Understanding.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data TinyBERT: Distilling BERT for Natural Language Understanding

Reference 19

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Observation 0408cb9e-6d7c-4e9e-8e57-fdb95922b328 · outbound

This paper cites Advances and open problems in federated learning.Foundations and trends® in machine learning, 14(1–2):1–210, 2021.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Advances and open problems in federated learning.Foundations and trends® in machine learning, 14(1–2):1–210, 2021

Reference 20

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Observation 839c3832-b04a-4d73-ab57-c5fe28ee1d8a · outbound

This paper cites Scaffold: Stochastic controlled averaging for federated learning.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Scaffold: Stochastic controlled averaging for federated learning

Reference 21

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Observation b3baf98f-ce9a-4923-a93c-fae5250b06e9 · outbound

This paper cites The Multilingual Amazon Reviews Corpus.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data The Multilingual Amazon Reviews Corpus

Reference 22

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Observation cfce5ad8-63ca-405d-95d5-ac7ec2fe944c · outbound

This paper cites Learning multiple layers of features from tiny im- ages.(2009), 2009.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Learning multiple layers of features from tiny im- ages.(2009), 2009

Reference 23

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Observation c437739f-4c3e-4780-b3e2-6ada7878efc0 · outbound

This paper cites FedMD: Heterogenous Federated Learning via Model Distillation.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data FedMD: Heterogenous Federated Learning via Model Distillation

Reference 24

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This paper cites Model-contrastive federated learning.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Model-contrastive federated learning

Reference 25

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Observation 0b941f71-8b78-404f-81e1-7f37ffe83d42 · outbound

This paper cites Federated optimization in heterogeneous networks.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Federated optimization in heterogeneous networks

Reference 26

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This paper cites Ditto: Fair and robust feder- ated learning through personalization.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Ditto: Fair and robust feder- ated learning through personalization

Reference 27

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Observation 02f2599e-baf8-4bd5-a8bc-d366c63f0d3e · outbound

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

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data On the Convergence of FedAvg on Non-IID Data

Reference 28

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Observation bf91abac-08d4-4df1-89ac-f10f13e6fe43 · outbound

This paper cites FedBN: Federated Learning on Non-IID Features via Local Batch Normalization.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data FedBN: Federated Learning on Non-IID Features via Local Batch Normalization

Reference 29

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Observation ae2d2532-fb25-4b6c-8534-ae39de721872 · outbound

This paper cites TRGP: Trust Region Gradient Projection for Continual Learning.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data TRGP: Trust Region Gradient Projection for Continual Learning

Reference 30

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This paper cites Ensemble distillation for robust model fusion in federated learning.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Ensemble distillation for robust model fusion in federated learning

Reference 31

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Observation 14686114-5018-42fa-802a-b828b7ed7a30 · outbound

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

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Communication-efficient learning of deep networks from decentralized data

Reference 32

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Observation 71ef67d9-cb7c-4a42-b44a-2083ef3b1e3f · outbound

This paper cites A practical recipe for federated learning under statistical heterogeneity experimental design.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data A practical recipe for federated learning under statistical heterogeneity experimental design

Reference 33

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Observation c0d13421-51c4-4637-baec-fe6d8bb06a4c · outbound

This paper cites Large scale delocalized federated learning over a huge diversity of devices in emerging next-generation edge intelligence environments.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Large scale delocalized federated learning over a huge diversity of devices in emerging next-generation edge intelligence environments

Reference 34

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Observation 6f35b7b6-efaf-43b9-b5e0-8cd2915e27f9 · outbound

This paper cites Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data

Reference 35

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This paper cites Clustered federated learning: A review.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Clustered federated learning: A review

Reference 36

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Observation ff934c8b-7da8-4fc8-b754-61b17a4aa5a4 · outbound

This paper cites Continual learning with scaled gradient projection.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Continual learning with scaled gradient projection

Reference 37

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Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Gradient Projection Memory for Continual Learning

Reference 38

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Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Relaxed contrastive learning for federated learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:39.740207Z

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This paper cites Personalized federated learning using hypernetworks.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Personalized federated learning using hypernetworks

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:39.618982Z

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This paper cites Recursive deep models for semantic compositionality over a sen- timent treebank.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Recursive deep models for semantic compositionality over a sen- timent treebank

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:39.491056Z

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This paper cites Tackling the ob- jective inconsistency problem in heterogeneous federated optimization.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Tackling the ob- jective inconsistency problem in heterogeneous federated optimization

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:39.400874Z

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Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Uncertainty estimation and reduction of pre-trained models for text regression

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:39.294833Z

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This paper cites A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

Reference 44

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This paper cites Minibatch vs local sgd for het- erogeneous distributed learning.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Minibatch vs local sgd for het- erogeneous distributed learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:39.154562Z

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This paper cites Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis

Reference 46

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This paper cites Federated continual learning via knowledge fusion: A survey.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Federated continual learning via knowledge fusion: A survey

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:39.023029Z

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Observation 31ac9f1c-c774-4eb7-8f6c-03abe6279fbc · outbound

This paper cites Continual learning of context-dependent processing in neural networks.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Continual learning of context-dependent processing in neural networks

Reference 48

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Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Character-level convolutional networks for text classification

Reference 49

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This paper cites Data-free knowledge distillation for hetero- geneous federated learning.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Data-free knowledge distillation for hetero- geneous federated learning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:38.762928Z

Source-reported events for the cited work

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Observation 3fb6f0a8-624d-4f55-89cb-e48958d75463 · outbound

This paper cites Data-free knowledge distillation for hetero- geneous federated learning.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Data-free knowledge distillation for hetero- geneous federated learning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:38.544349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation 08dc3db0-6aa5-4d5a-9076-589c5b19fc0c · inbound

Dissecting Federated-Graph Aggregation under Domain Shift: Importance-Aware Aggregation via Empirical Analysis cites this paper.

Dissecting Federated-Graph Aggregation under Domain Shift: Importance-Aware Aggregation via Empirical Analysis Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T16:34:00.285002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.