Paper Citation Record · LEDGER

Collaborative Batch Size Optimization for Federated Learning

As of 17 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2506.20511.

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

pith.paper-citation-record.v1
2506.20511 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:51:57.075091Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

20 of 20 outbound references displayed

verified exact1
verified fuzzy7
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a1b72bd1-0930-4bde-adfb-9b86d7639b1f · outbound

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

Collaborative Batch Size Optimization for Federated Learning Communication-efficient learning of deep networks from decentralized data,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T22:51:55.532715Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0acb25a2-93b4-469e-bf54-6fccf8f252a1 · outbound

This paper cites A review of applications in federated learning,.

Collaborative Batch Size Optimization for Federated Learning A review of applications in federated learning,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T22:51:55.609808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73b53b2f-9f57-4cc7-a249-7c25c96fd95e · outbound

This paper cites Flowertune: A cross- domain benchmark for federated fine-tuning of large language models,.

Collaborative Batch Size Optimization for Federated Learning Flowertune: A cross- domain benchmark for federated fine-tuning of large language models,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T22:51:55.701224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16d3f22c-522a-4428-8ac6-2ad4fa476594 · outbound

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

Collaborative Batch Size Optimization for Federated Learning Don't Decay the Learning Rate, Increase the Batch Size

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T22:51:55.820139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a26138b-ad1b-45fc-b696-a1768ba37515 · outbound

This paper cites The Limit of the Batch Size.

Collaborative Batch Size Optimization for Federated Learning The Limit of the Batch Size

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:51:57.281610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fe86d478-e72d-4045-8ea3-5e8a8cc9fe5b · outbound

This paper cites Control batch size and learning rate to generalize well: Theoretical and empirical evidence,.

Collaborative Batch Size Optimization for Federated Learning Control batch size and learning rate to generalize well: Theoretical and empirical evidence,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:51:58.943884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 26f91fc4-2bf7-4c74-a186-3aac5ab086d4 · outbound

This paper cites Revisiting Small Batch Training for Deep Neural Networks.

Collaborative Batch Size Optimization for Federated Learning Revisiting Small Batch Training for Deep Neural Networks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T22:51:56.072963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b224a2e-02e6-44c1-a800-aa3937f5aa83 · outbound

This paper cites Adaptive batch size for federated learning in resource-constrained edge computing,.

Collaborative Batch Size Optimization for Federated Learning Adaptive batch size for federated learning in resource-constrained edge computing,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:51:58.717855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f9c8073a-96b2-42ac-afff-ed99f43e7cd4 · outbound

This paper cites Mergesfl: Split federated learning with feature merging and batch size regulation,.

Collaborative Batch Size Optimization for Federated Learning Mergesfl: Split federated learning with feature merging and batch size regulation,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T22:51:56.222767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85f6545f-5777-47ea-9e1e-2e038f223296 · outbound

This paper cites Dynamite: Dynamic interplay of mini-batch size and aggregation frequency for federated learning with static and streaming datasets,.

Collaborative Batch Size Optimization for Federated Learning Dynamite: Dynamic interplay of mini-batch size and aggregation frequency for federated learning with static and streaming datasets,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T22:51:56.314054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b089c50-e0c4-4e02-936b-a4b13f57130b · outbound

This paper cites Adaptive batchsize selection and gradient compression for wireless federated learning,.

Collaborative Batch Size Optimization for Federated Learning Adaptive batchsize selection and gradient compression for wireless federated learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:51:58.447942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 07c846b9-4487-4e04-9bb1-c2d0750d9911 · outbound

This paper cites Ada- coopt: Leverage the interplay of batch size and aggregation frequency for federated learning,.

Collaborative Batch Size Optimization for Federated Learning Ada- coopt: Leverage the interplay of batch size and aggregation frequency for federated learning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:51:58.178333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 67d80071-bf2c-449c-959e-626e210aba8f · outbound

This paper cites To talk or to work: Dynamic batch sizes assisted time efficient federated learn- ing over future mobile edge devices,.

Collaborative Batch Size Optimization for Federated Learning To talk or to work: Dynamic batch sizes assisted time efficient federated learn- ing over future mobile edge devices,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:51:57.938995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e3ee9937-9e25-4e72-a6f7-b7e83fe7deee · outbound

This paper cites Amble: Adjusting mini-batch and local epoch for federated learning with heterogeneous devices,.

Collaborative Batch Size Optimization for Federated Learning Amble: Adjusting mini-batch and local epoch for federated learning with heterogeneous devices,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:51:57.774884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 56d370b7-c486-4a53-a183-e1659be85509 · outbound

This paper cites Flower: A Friendly Federated Learning Research Framework.

Collaborative Batch Size Optimization for Federated Learning Flower: A Friendly Federated Learning Research Framework

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T22:51:56.652837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6996f40a-dbf3-4001-8ad4-449f94394b59 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Collaborative Batch Size Optimization for Federated Learning MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T22:51:56.736839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d67b4331-a82d-4bcf-88c5-ab26384ccdc0 · outbound

This paper cites The mnist database of handwritten digit images for machine learning research [best of the web],.

Collaborative Batch Size Optimization for Federated Learning The mnist database of handwritten digit images for machine learning research [best of the web],

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:51:57.631480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a8850663-4ef7-4af5-856a-9f4568d3f46a · outbound

This paper cites Deep residual learning for image recognition,.

Collaborative Batch Size Optimization for Federated Learning Deep residual learning for image recognition,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T22:51:56.864438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b7a4b31-5784-494e-a1b3-60a585b4f04d · outbound

This paper cites Learning multiple layers of features from tiny images,.

Collaborative Batch Size Optimization for Federated Learning Learning multiple layers of features from tiny images,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T22:51:56.978098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3cf2f8c-9f0b-4583-9b2a-3aca53222a3b · outbound

This paper cites Federated Learning with Matched Averaging.

Collaborative Batch Size Optimization for Federated Learning Federated Learning with Matched Averaging

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T22:51:57.075091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.