Paper Citation Record · LEDGER

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data

As of 7 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2506.08167.

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

pith.paper-citation-record.v1
2506.08167 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:22:27.731844Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

30 of 30 outbound references displayed

verified exact1
verified fuzzy18
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f420d6db-24c9-4600-a365-cedc11ee5b3d · outbound

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

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Communication- efficient learning of deep networks from decentralized data

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:26.992592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22dec682-6fbf-4c57-8a4f-4835e54c534a · outbound

This paper cites A survey on distributed machine learning.Acm computing surveys (csur), 53(2):1–33, 2020.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data A survey on distributed machine learning.Acm computing surveys (csur), 53(2):1–33, 2020

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:28.189053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b6a85448-1fa7-4272-bd00-6744c36ec56f · outbound

This paper cites Federated Learning Based on Dynamic Regularization.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Federated Learning Based on Dynamic Regularization

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:27.159468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3530b291-3048-4587-b619-1f5ad9682edd · outbound

This paper cites Model-contrastive federated learning.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Model-contrastive federated learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:28.173294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c4129e21-b033-439e-a878-91a626c6b1e5 · outbound

This paper cites Federated optimization in heterogeneous networks.Proceedings of Machine learning and systems, 2:429–450, 2020.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Federated optimization in heterogeneous networks.Proceedings of Machine learning and systems, 2:429–450, 2020

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:27.299943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36c09f5c-271e-451d-be9b-ffa98601061e · outbound

This paper cites No fear of heterogeneity: Classifier calibration for federated learning with non-iid data.Advances in Neural Information Processing Systems, 34:5972– 5984, 2021.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data No fear of heterogeneity: Classifier calibration for federated learning with non-iid data.Advances in Neural Information Processing Systems, 34:5972– 5984, 2021

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:28.146022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8224bc1c-4d8a-4147-8ac0-df3466458807 · outbound

This paper cites Gradaug: A new regularization method for deep neural networks.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Gradaug: A new regularization method for deep neural networks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:28.131108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 259b3610-ebc8-4aaa-a793-db16e21b977f · outbound

This paper cites FedBABU: Towards Enhanced Representation for Federated Image Classification.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data FedBABU: Towards Enhanced Representation for Federated Image Classification

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:27.547627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 802e5cb9-bbfd-4155-9947-7eac5a5bb0e6 · outbound

This paper cites Deep residual learning for image recognition.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Deep residual learning for image recognition

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:27.644890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9964bd1-3dd0-4100-a0e8-bf80492a68da · outbound

This paper cites Learning multiple layers of features from tiny images.https://www.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Learning multiple layers of features from tiny images.https://www

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:28.105093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b0b8ecee-fc01-4c25-88f0-2adfe61666b5 · outbound

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

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Scaffold: Stochastic controlled averaging for federated learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:28.090601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 31b4f39b-cc08-49bf-8a00-18bf111eec2e · outbound

This paper cites Local learning matters: Rethinking data heterogeneity in federated learning.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Local learning matters: Rethinking data heterogeneity in federated learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:28.075938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e93b8e36-9185-46a9-bc9b-1c3a7e242225 · outbound

This paper cites Is your data relevant?: Dynamic selection of relevant data for federated learning.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Is your data relevant?: Dynamic selection of relevant data for federated learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:28.061855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fa1b280c-1d17-4f7c-a701-7d1b88a6f137 · outbound

This paper cites Fedcor: Correlation-based active client selection strategy for heterogeneous federated learning.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Fedcor: Correlation-based active client selection strategy for heterogeneous federated learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:28.047727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 55674224-e855-46a7-8d06-65dea2983499 · outbound

This paper cites FedMix: Approximation of Mixup under Mean Augmented Federated Learning.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data FedMix: Approximation of Mixup under Mean Augmented Federated Learning

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:22:27.855725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7f1f83f0-1a8a-4edb-993e-07f691327070 · outbound

This paper cites Differentially private federated learning with local regularization and sparsification.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Differentially private federated learning with local regularization and sparsification

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:28.033311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 25cecf19-f81e-477b-8236-a2198692a9cc · outbound

This paper cites Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:27.678018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a32bddb7-ca3c-462d-9463-00f4e1f9e472 · outbound

This paper cites HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:27.682498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8d93488-3b86-4784-a702-3002f6ffd7ae · outbound

This paper cites Smartidx: Reducing communication cost in federated learning by exploiting the cnns structures.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Smartidx: Reducing communication cost in federated learning by exploiting the cnns structures

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:28.017865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 372ad66e-2396-4d8a-be78-445c199da80e · outbound

This paper cites Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:27.690777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0632c1b-2459-4bae-8f35-793b07ca115a · outbound

This paper cites Closing the generalization gap of cross-silo federated medical image segmentation.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Closing the generalization gap of cross-silo federated medical image segmentation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:28.001023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7134d1ce-ff7f-4641-8dd7-5b18b926eb59 · outbound

This paper cites Cd2-pfed: Cyclic distillation-guided channel decoupling for model personalization in federated learning.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Cd2-pfed: Cyclic distillation-guided channel decoupling for model personalization in federated learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:27.986391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 50931ec5-8d30-44f5-a298-8ef3857dc332 · outbound

This paper cites Fine-tuning global model via data-free knowledge distillation for non-iid federated learning.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Fine-tuning global model via data-free knowledge distillation for non-iid federated learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:27.971552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e61c0195-6f74-46a4-a954-970ead77d506 · outbound

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

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:27.707228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a37dd833-37f0-4376-9ab2-f685c26b11f4 · outbound

This paper cites Layer-wised model aggregation for personalized federated learning.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Layer-wised model aggregation for personalized federated learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:27.957024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f3279043-b262-4dc4-81ac-d43c2ceb2376 · outbound

This paper cites Personalized federated learning using hypernetworks.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Personalized federated learning using hypernetworks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:27.942516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c8fd2a7f-c351-4964-99f7-a04612e8d014 · outbound

This paper cites Bayesian nonparametric federated learning of neural networks.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Bayesian nonparametric federated learning of neural networks

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:27.719772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92c28f66-6363-494f-b0ae-4d2d3bee812e · outbound

This paper cites Federated Learning with Matched Averaging.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Federated Learning with Matched Averaging

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:27.723694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8dfa4be-1f44-4e2b-b4ab-aee65b24a1c9 · outbound

This paper cites Federated learning with position-aware neurons.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Federated learning with position-aware neurons

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:27.917966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e9673791-1eea-42df-b6b4-e11652d73d1f · outbound

This paper cites Ensemble distillation for robust model fusion in federated learning.Advances in neural information processing systems, 33:2351–2363, 2020.

UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data Ensemble distillation for robust model fusion in federated learning.Advances in neural information processing systems, 33:2351–2363, 2020

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:27.903575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.