Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:15:29.794243Z
Paper Citation Record · LEDGER
As of 9 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.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:15:29.794243Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
28 of 28 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 857a9b98-f88a-4b6c-982a-e740b0e91341 · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 0e809983-f09c-472e-84b7-f75ee79de53c · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation f2047322-f844-483d-a22d-4a56d6240b95 · outbound
Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Emnist: Extending mnist to handwritten letters
Reference 3
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e1b5e723-0f6a-4bc4-9fb6-613750277889 · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 7554e134-6579-4cc4-b377-f99300a69ed6 · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 0b7d2e83-fe40-4425-b097-4c81c7120005 · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation ffff6662-1d7f-4e1e-bd31-edfe16df0716 · outbound
Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Novel insights into lossless ac and dc power flow
Reference 7
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 0fc0549a-8388-48d6-8801-7c6e512bc642 · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 167678b3-d1ef-4093-a623-f482c3c3b06f · outbound
Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Scaffold: Stochastic controlled averaging for federated learning
Reference 9
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a602792d-93c6-499a-a817-f2b384078327 · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation f532bf49-3da1-491d-93ed-2b27dd22a085 · outbound
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 51c6317f-92c7-4a99-b284-3331b21d7304 · outbound
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 82cb91e8-d841-4940-beba-c9701f17e42c · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 85d638ba-e531-4b4e-b584-7cf845d7dd56 · outbound
Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Communication-efficient learning of deep networks from decentralized data
Reference 14
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 42937406-698a-46af-887d-e1fc26e22450 · outbound
Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity An Introduction to Convolutional Neural Networks
Reference 15
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 21098513-5c0e-4d36-898a-4a17f312ae57 · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 4d74f531-bed8-47e4-a592-4d10284c8793 · outbound
Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Do CIFAR-10 Classifiers Generalize to CIFAR-10?
Reference 17
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation aafb3c1f-5a28-467f-8aa4-ebaefeaa97ef · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation fcee1be4-4204-42fb-b7cb-65e397b49626 · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 8d8c2595-3632-4df2-8e0c-790201cc8e39 · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 124f1d25-7f55-4a5a-826d-812d7e5bd3b5 · outbound
Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Sync: The emerging science of spontaneous order
Reference 21
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 52fd7e09-db83-4e6c-8f84-776dd16eb3ed · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 365b56f0-40c5-4265-b8e6-82d9931ba2f9 · outbound
Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Unresolved cited work
Reference 23
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation a850b5e5-57ad-43d2-bcf5-9af45db1c423 · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 8043a969-1022-41f8-9230-4facc1696c86 · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 284801e1-6d62-4a6e-895a-264d5ef0a64c · outbound
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 06067c36-3a4b-49d2-8578-a16d283c3283 · outbound
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1204dcd6-3c86-4154-acd2-b78af41f33e4 · outbound
Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity Federated Learning with Non-IID Data
Reference 28
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
No inbound Pith citation observations are available.