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Paper Citation Record · LEDGER

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity

As of 20 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2505.19605.

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

pith.paper-citation-record.v1
2505.19605 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:15:29.794243Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

28 of 28 outbound references displayed

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External citation measurements

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Outbound references

Observation 857a9b98-f88a-4b6c-982a-e740b0e91341 · outbound

This paper cites The kuramoto model: A simple paradigm for synchronization phenomena.Reviews of modern physics, 77(1):137–185, 2005.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity The kuramoto model: A simple paradigm for synchronization phenomena.Reviews of modern physics, 77(1):137–185, 2005

Reference 1

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Source-reported events for the cited work

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Observation 0e809983-f09c-472e-84b7-f75ee79de53c · outbound

This paper cites Syn- chronization in complex networks.Physics reports, 469(3):93–153, 2008.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Syn- chronization in complex networks.Physics reports, 469(3):93–153, 2008

Reference 2

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation f2047322-f844-483d-a22d-4a56d6240b95 · outbound

This paper cites Emnist: Extending mnist to handwritten letters.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Emnist: Extending mnist to handwritten letters

Reference 3

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Source-reported events for the cited work

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Observation e1b5e723-0f6a-4bc4-9fb6-613750277889 · outbound

This paper cites Kuramoto model with frequency- degree correlations on complex networks.Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 87(3):032106, 2013.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Kuramoto model with frequency- degree correlations on complex networks.Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 87(3):032106, 2013

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 7554e134-6579-4cc4-b377-f99300a69ed6 · outbound

This paper cites Amplitude expansions for instabilities in populations of globally-coupled oscillators.Journal of statistical physics, 74:1047–1084, 1994.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Amplitude expansions for instabilities in populations of globally-coupled oscillators.Journal of statistical physics, 74:1047–1084, 1994

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 0b7d2e83-fe40-4425-b097-4c81c7120005 · outbound

This paper cites Synchronization and transient stability in power networks and nonuniform kuramoto oscillators.SIAM Journal on Control and Optimization, 50(3):1616– 1642, 2012.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Synchronization and transient stability in power networks and nonuniform kuramoto oscillators.SIAM Journal on Control and Optimization, 50(3):1616– 1642, 2012

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation ffff6662-1d7f-4e1e-bd31-edfe16df0716 · outbound

This paper cites Novel insights into lossless ac and dc power flow.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Novel insights into lossless ac and dc power flow

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:15:27.500461Z digest=sha256:b1c1f9b9c0af4672e80c3919ed3d091970cdc0b9aa48912f974cfaf3d3787606

Observation 0fc0549a-8388-48d6-8801-7c6e512bc642 · outbound

This paper cites An adaptive model for synchrony in the firefly pteroptyx malaccae.Journal of Mathematical Biology, 29(6):571–585, 1991.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity An adaptive model for synchrony in the firefly pteroptyx malaccae.Journal of Mathematical Biology, 29(6):571–585, 1991

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 167678b3-d1ef-4093-a623-f482c3c3b06f · outbound

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

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Scaffold: Stochastic controlled averaging for federated learning

Reference 9

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Observation a602792d-93c6-499a-a817-f2b384078327 · outbound

This paper cites Self-entrainment of a population of coupled non-linear oscillators.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Self-entrainment of a population of coupled non-linear oscillators

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation f532bf49-3da1-491d-93ed-2b27dd22a085 · outbound

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

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Federated optimization in heterogeneous networks.Proceedings of Machine learning and systems, 2:429–450, 2020

Reference 11

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Observation 51c6317f-92c7-4a99-b284-3331b21d7304 · outbound

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

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity FedBN: Federated Learning on Non-IID Features via Local Batch Normalization

Reference 12

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Observation 82cb91e8-d841-4940-beba-c9701f17e42c · outbound

This paper cites Synchronization in the random- field kuramoto model on complex networks.Physical Review E, 94(1):012308, 2016.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Synchronization in the random- field kuramoto model on complex networks.Physical Review E, 94(1):012308, 2016

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 85d638ba-e531-4b4e-b584-7cf845d7dd56 · outbound

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

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Communication-efficient learning of deep networks from decentralized data

Reference 14

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Observation 42937406-698a-46af-887d-e1fc26e22450 · outbound

This paper cites An Introduction to Convolutional Neural Networks.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity An Introduction to Convolutional Neural Networks

Reference 15

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Observation 21098513-5c0e-4d36-898a-4a17f312ae57 · outbound

This paper cites Network dynamics of coupled oscillators and phase reduction techniques.Physics Reports, 819:1–105, 2019.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Network dynamics of coupled oscillators and phase reduction techniques.Physics Reports, 819:1–105, 2019

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 4d74f531-bed8-47e4-a592-4d10284c8793 · outbound

This paper cites Do CIFAR-10 Classifiers Generalize to CIFAR-10?.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Do CIFAR-10 Classifiers Generalize to CIFAR-10?

Reference 17

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Observation aafb3c1f-5a28-467f-8aa4-ebaefeaa97ef · outbound

This paper cites The kuramoto model in complex networks.Physics Reports, 610:1–98, 2016.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity The kuramoto model in complex networks.Physics Reports, 610:1–98, 2016

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation fcee1be4-4204-42fb-b7cb-65e397b49626 · outbound

This paper cites Understanding Federated Learning from IID to Non-IID dataset: An Experimental Study.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Understanding Federated Learning from IID to Non-IID dataset: An Experimental Study

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 8d8c2595-3632-4df2-8e0c-790201cc8e39 · outbound

This paper cites Higher order interactions in complex networks of phase oscillators promote abrupt synchronization switching.Communications Physics, 3(1):218, 2020.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Higher order interactions in complex networks of phase oscillators promote abrupt synchronization switching.Communications Physics, 3(1):218, 2020

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 124f1d25-7f55-4a5a-826d-812d7e5bd3b5 · outbound

This paper cites Sync: The emerging science of spontaneous order.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Sync: The emerging science of spontaneous order

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:15:28.922716Z digest=sha256:51bd08461abbc6d656226d81777ef6b3c1796a24a12094ad5d3609e4af800abb

Observation 52fd7e09-db83-4e6c-8f84-776dd16eb3ed · outbound

This paper cites From kuramoto to crawford: exploring the onset of synchronization in populations of coupled oscillators.Physica D: Nonlinear Phenomena, 143(1-4):1–20, 2000.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity From kuramoto to crawford: exploring the onset of synchronization in populations of coupled oscillators.Physica D: Nonlinear Phenomena, 143(1-4):1–20, 2000

Reference 22

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 365b56f0-40c5-4265-b8e6-82d9931ba2f9 · outbound

This paper cites an unresolved cited work.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Unresolved cited work

Reference 23

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation a850b5e5-57ad-43d2-bcf5-9af45db1c423 · outbound

This paper cites Tackling the objective inconsistency problem in heterogeneous federated optimization.Advances in neural information processing systems, 33:7611–7623, 2020.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Tackling the objective inconsistency problem in heterogeneous federated optimization.Advances in neural information processing systems, 33:7611–7623, 2020

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 8043a969-1022-41f8-9230-4facc1696c86 · outbound

This paper cites Synchronization transitions in a disordered josephson series array.Physical review letters, 76(3):404, 1996.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Synchronization transitions in a disordered josephson series array.Physical review letters, 76(3):404, 1996

Reference 25

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raw_fallback, observed 2026-08-07T14:15:30.450597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 284801e1-6d62-4a6e-895a-264d5ef0a64c · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 26

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Observation 06067c36-3a4b-49d2-8578-a16d283c3283 · outbound

This paper cites Federated machine learning: Concept and applications.ACM Transactions on Intelligent Systems and Technology (TIST), 10(2):1–19, 2019.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Federated machine learning: Concept and applications.ACM Transactions on Intelligent Systems and Technology (TIST), 10(2):1–19, 2019

Reference 27

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Observation 1204dcd6-3c86-4154-acd2-b78af41f33e4 · outbound

This paper cites Federated Learning with Non-IID Data.

Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Federated Learning with Non-IID Data

Reference 28

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Pith citing papers

No inbound Pith citation observations are available.