Paper Citation Record · LEDGER

Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning

As of 21 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2505.21877.

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

pith.paper-citation-record.v1
2505.21877 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:25:58.414610Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

18 of 18 outbound references displayed

verified exact2
verified fuzzy1
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a260ead7-0b6a-4539-8a85-47227904dae7 · outbound

This paper cites an unresolved cited work.

Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:25:59.237174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3b61dc53-b3d2-4113-a69b-1a183867068e · outbound

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

Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T13:25:57.576103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e7793f9-45ac-4567-b840-3915d0abfebc · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T13:25:57.601772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f070e04b-0894-48d8-bc3a-6b28ba13a1ff · outbound

This paper cites On the Convergence of FedAvg on Non-IID Data.

Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning On the Convergence of FedAvg on Non-IID Data

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T13:25:57.795669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14904058-8664-44fe-bd2d-56fc336a2fc2 · outbound

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

Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning FedBN: Federated Learning on Non-IID Features via Local Batch Normalization

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T13:25:57.875187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9b334d7-c408-4d4b-8496-8bc81c4ec505 · outbound

This paper cites Continual Normalization: Rethinking Batch Normalization for Online Continual Learning.

Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning Continual Normalization: Rethinking Batch Normalization for Online Continual Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T13:25:58.030420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f861f45f-d96e-4dc5-b911-505a5563a0fc · outbound

This paper cites FedCM: Federated Learning with Client-level Momentum.

Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning FedCM: Federated Learning with Client-level Momentum

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T13:25:58.192857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9421f817-0238-43cd-9dd6-c6e99fe02c4a · outbound

This paper cites Federated Learning with Non-IID Data.

Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning Federated Learning with Non-IID Data

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T13:25:58.260358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f5b42d9-34c5-472e-9474-bd559f4f0faf · outbound

This paper cites One weird trick for parallelizing convolutional neural networks.

Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning One weird trick for parallelizing convolutional neural networks

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-07T13:25:57.743509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c801221-a63a-44f0-8e61-73dd3433095f · outbound

This paper cites BN can effectively solve this problem under centralised training.

Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning BN can effectively solve this problem under centralised training

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:25:59.413321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6b54a4a4-0574-4f47-ac24-d57375913f74 · outbound

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

Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T13:25:57.332477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dc08a57-88c9-4593-b58e-e8ea3830e5a2 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-07T13:25:58.168578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation daf1481a-83d8-40be-a6f5-8c8443f47bec · outbound

This paper cites Overcoming the Challenges of Batch Normalization in Federated Learning.

Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning Overcoming the Challenges of Batch Normalization in Federated Learning

Reference 2019

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:25:58.917175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e745b141-a4cb-45f2-8324-7c35a7f0a9e0 · outbound

This paper cites Layer Normalization.

Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning Layer Normalization

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T13:25:57.220001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69d7d38f-6491-4fca-a143-2b43c5474962 · outbound

This paper cites Towards Understanding Regularization in Batch Normalization.

Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning Towards Understanding Regularization in Batch Normalization

Reference 2021

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:25:58.647561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation db6f2a4b-acde-462e-9943-9cf2f24b8472 · outbound

This paper cites Adaptive Federated Optimization.

Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning Adaptive Federated Optimization

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T13:25:58.104149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bc75cca-d6d7-4347-8e93-e86f0452742f · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning Adam: A Method for Stochastic Optimization

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T13:25:57.675730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afad6780-c25b-45ed-80c0-0503a22f1a5b · outbound

This paper cites A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets.

Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T13:25:57.263132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.