Paper Citation Record · LEDGER

On Using Large-Batches in Federated Learning

As of 22 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2509.10537.

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

pith.paper-citation-record.v1
2509.10537 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:28:36.683733Z

measured 37 of 37 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

37 of 37 outbound references displayed

verified exact2
verified fuzzy26
unresolved9
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9f27a6d7-05cc-4d71-ba0b-be2e110f3445 · outbound

This paper cites Communication-Efficient Learning of Deep Net- works from Decentralized Data.

On Using Large-Batches in Federated Learning Communication-Efficient Learning of Deep Net- works from Decentralized Data

Reference 1

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Observation 4d62f2b5-3648-41ff-b8f8-1c207f806482 · outbound

This paper cites Accelerating Distributed ML Training via Selective Synchronization.

On Using Large-Batches in Federated Learning Accelerating Distributed ML Training via Selective Synchronization

Reference 2

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Observation 6b1a2d1c-c697-4223-af37-2aa2a83b4211 · outbound

This paper cites Flexible Communication for Optimal Distributed Learning over Unpredictable Networks.

On Using Large-Batches in Federated Learning Flexible Communication for Optimal Distributed Learning over Unpredictable Networks

Reference 3

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Observation b42dcd2d-8447-48ad-b7f7-bb18817aea60 · outbound

This paper cites ImageNet Training in Minutes.

On Using Large-Batches in Federated Learning ImageNet Training in Minutes

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:38.445898Z

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Observation 5edee915-7d9f-4b99-abad-36a499dfcbb9 · outbound

This paper cites An Empirical Model of Large-Batch Training.

On Using Large-Batches in Federated Learning An Empirical Model of Large-Batch Training

Reference 5

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Observation 275a8132-7b47-43dd-ab6c-c9f93e50574f · outbound

This paper cites Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

On Using Large-Batches in Federated Learning Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Reference 6

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Source-reported events for the cited work

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Observation 640cda5d-3331-4b28-a398-f766c6b59931 · outbound

This paper cites An Empirical Study of Large-Batch Stochastic Gradient Descent with Structured Covariance Noise.

On Using Large-Batches in Federated Learning An Empirical Study of Large-Batch Stochastic Gradient Descent with Structured Covariance Noise

Reference 7

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Observation 185ca81c-8dca-4d49-9b4c-17c60d67b6fe · outbound

This paper cites Distributed Deep Learning Using Synchronous Stochastic Gradient Descent.

On Using Large-Batches in Federated Learning Distributed Deep Learning Using Synchronous Stochastic Gradient Descent

Reference 8

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Observation 1f0aea62-e19d-40db-997c-d6f46aa0ab7a · outbound

This paper cites Train longer, generalize better: closing the general- ization gap in large batch training of neural networks.

On Using Large-Batches in Federated Learning Train longer, generalize better: closing the general- ization gap in large batch training of neural networks

Reference 9

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Observation 8be5ec3d-90ab-4949-a129-4408c4d63867 · outbound

This paper cites Extrapolation for Large-batch Training in Deep Learn- ing.

On Using Large-Batches in Federated Learning Extrapolation for Large-batch Training in Deep Learn- ing

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:38.225175Z

Source-reported events for the cited work

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Observation 85961323-d0dd-433d-83a9-c4b42cb9ae18 · outbound

This paper cites On Large-Batch Training for Deep Learn- ing: Generalization Gap and Sharp Minima.

On Using Large-Batches in Federated Learning On Large-Batch Training for Deep Learn- ing: Generalization Gap and Sharp Minima

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:38.131169Z

Source-reported events for the cited work

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Observation a6f9ef5a-f9fe-48c9-9833-9beda05a4679 · outbound

This paper cites Hessian-based Analysis of Large Batch Training and Robustness to Adversaries.

On Using Large-Batches in Federated Learning Hessian-based Analysis of Large Batch Training and Robustness to Adversaries

Reference 12

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Source-reported events for the cited work

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Observation b5f9dea6-a373-4803-ba41-11fba6331565 · outbound

This paper cites Learned Gradient Compression for Dis- tributed Deep Learning.

On Using Large-Batches in Federated Learning Learned Gradient Compression for Dis- tributed Deep Learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:38.043127Z

Source-reported events for the cited work

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Observation 48193964-1477-4a44-918f-f6ed2bb0a738 · outbound

This paper cites Learned Parameter Compression for Efficient and Privacy-Preserving Federated Learning.

On Using Large-Batches in Federated Learning Learned Parameter Compression for Efficient and Privacy-Preserving Federated Learning

Reference 14

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Source-reported events for the cited work

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Observation ee55593e-340b-4fd1-b666-1e8927c9296d · outbound

This paper cites Scavenger: A Cloud Service For Optimizing Cost and Performance of ML Training.

On Using Large-Batches in Federated Learning Scavenger: A Cloud Service For Optimizing Cost and Performance of ML Training

Reference 15

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Source-reported events for the cited work

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Observation bca7c446-f135-4fe6-a45b-42c9bb57de6e · outbound

This paper cites Large batch size training of neural networks with adversarial training and second-order information.

On Using Large-Batches in Federated Learning Large batch size training of neural networks with adversarial training and second-order information

Reference 16

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Source-reported events for the cited work

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Observation d81b6bd9-0c89-48db-8b13-a6dde72b1c73 · outbound

This paper cites Large Batch Training of Convolutional Networks.

On Using Large-Batches in Federated Learning Large Batch Training of Convolutional Networks

Reference 17

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Source-reported events for the cited work

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Observation b3a51d98-4d6b-4c89-845c-35e2d36d3786 · outbound

This paper cites Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes.

On Using Large-Batches in Federated Learning Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:37.872705Z

Source-reported events for the cited work

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Observation a3e0ad30-1be6-4e15-a511-e588415a647f · outbound

This paper cites Large Batch Optimization for Deep Learning: Training BERT in 76 minutes.

On Using Large-Batches in Federated Learning Large Batch Optimization for Deep Learning: Training BERT in 76 minutes

Reference 19

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Source-reported events for the cited work

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Observation 7982ab7e-7e38-4fcd-9db9-b7ec549097f0 · outbound

This paper cites Don’t Decay the Learning Rate, Increase the Batch Size.

On Using Large-Batches in Federated Learning Don’t Decay the Learning Rate, Increase the Batch Size

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:37.693266Z

Source-reported events for the cited work

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Observation 8a1e5646-92bc-42f9-885a-628d95540735 · outbound

This paper cites Don’t Use Large Mini-Batches, Use Local SGD.

On Using Large-Batches in Federated Learning Don’t Use Large Mini-Batches, Use Local SGD

Reference 21

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Observation c3810584-278d-4296-9100-d3aaeea5f0c0 · outbound

This paper cites ScaDLES: Scalable Deep Learning over Streaming data at the Edge.

On Using Large-Batches in Federated Learning ScaDLES: Scalable Deep Learning over Streaming data at the Edge

Reference 22

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Source-reported events for the cited work

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Observation 33a8c8a2-1e3e-40d8-bec4-792a4829a5ca · outbound

This paper cites Adaptive Federated Learning in Resource Con- strained Edge Computing Systems.

On Using Large-Batches in Federated Learning Adaptive Federated Learning in Resource Con- strained Edge Computing Systems

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:37.607384Z

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 b12cf58e-36ef-4662-a0f9-d2b271f8315c · outbound

This paper cites Federated Learning: Challenges, Methods, and Future Directions.

On Using Large-Batches in Federated Learning Federated Learning: Challenges, Methods, and Future Directions

Reference 24

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

Observation 2b445caa-19ff-42f1-99d2-550517628170 · outbound

This paper cites Apple Intelligence Foundation Language Models.

On Using Large-Batches in Federated Learning Apple Intelligence Foundation Language Models

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:37.502072Z

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 efc13c71-1dc5-4d17-9667-098e403c8ca8 · outbound

This paper cites Decentralized Federated Learning: A Survey and Perspective.

On Using Large-Batches in Federated Learning Decentralized Federated Learning: A Survey and Perspective

Reference 26

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Observation faf29043-9b79-4917-a56c-0935c741276c · outbound

This paper cites GraV AC: Adaptive Compression for Communication-Efficient Distributed DL Training.

On Using Large-Batches in Federated Learning GraV AC: Adaptive Compression for Communication-Efficient Distributed DL Training

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:37.359964Z

Source-reported events for the cited work

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Observation 75eececc-1506-4abb-b6ad-1af5a3702794 · outbound

This paper cites ZeRO: Memory Optimizations Toward Training Trillion Parameter Models.

On Using Large-Batches in Federated Learning ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

Reference 28

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Source-reported events for the cited work

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Observation b98b97b6-af9b-4deb-ab9f-b452dcb2d33b · outbound

This paper cites On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent.

On Using Large-Batches in Federated Learning On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T16:28:36.569894Z

Source-reported events for the cited work

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Observation 8e767a81-26a8-4688-90d7-0f79ba617c67 · outbound

This paper cites Revisiting LARS for Large Batch Training Generalization of Neural Networks.

On Using Large-Batches in Federated Learning Revisiting LARS for Large Batch Training Generalization of Neural Networks

Reference 30

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Source-reported events for the cited work

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Observation b92b35ac-d121-403a-b57b-da7671d807d2 · outbound

This paper cites A Bayesian Perspective on Generalization and Stochastic Gradient Descent.

On Using Large-Batches in Federated Learning A Bayesian Perspective on Generalization and Stochastic Gradient Descent

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:37.303116Z

Source-reported events for the cited work

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Observation f6a63d98-c016-422e-b4b1-b64b093384e7 · outbound

This paper cites Critical Learning Periods in Deep Neural Networks.

On Using Large-Batches in Federated Learning Critical Learning Periods in Deep Neural Networks

Reference 32

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Source-reported events for the cited work

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Observation a33b35a4-2a5f-49af-805e-c2a700dc33d5 · outbound

This paper cites The Early Phase of Neural Network Training.

On Using Large-Batches in Federated Learning The Early Phase of Neural Network Training

Reference 33

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Source-reported events for the cited work

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Observation 57f876fc-8282-440e-b518-78e7e78b6ed9 · outbound

This paper cites Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification.

On Using Large-Batches in Federated Learning Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:37.285590Z

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 63299074-01be-4dce-9ed8-da56ecfc5487 · outbound

This paper cites Taming Resource Heterogeneity In Distributed ML Training With Dynamic Batching.

On Using Large-Batches in Federated Learning Taming Resource Heterogeneity In Distributed ML Training With Dynamic Batching

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:37.266524Z

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 1d789dd7-09a5-423b-9c05-39d6c73f8728 · outbound

This paper cites OmniLearn: A Framework for Distributed Deep Learning Over Heterogeneous Clusters.

On Using Large-Batches in Federated Learning OmniLearn: A Framework for Distributed Deep Learning Over Heterogeneous Clusters

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:28:37.147826Z

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 293e9be2-60c5-4d74-ae4c-a33a8495ee39 · outbound

This paper cites ”An Overview of Computational and Communica- tion Mechanisms for Scalable AI Systems”.

On Using Large-Batches in Federated Learning ”An Overview of Computational and Communica- tion Mechanisms for Scalable AI Systems”

Reference 37

Resolution: verified exact; raw_fallback, observed 2026-08-15T16:28:36.797315Z

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.

Pith citing papers

No inbound Pith citation observations are available.