Paper Citation Record · LEDGER

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning

As of 22 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2506.12754.

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

pith.paper-citation-record.v1
2506.12754 v2

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:52:38.439341Z

measured 38 of 38 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

38 of 38 outbound references displayed

verified exact7
verified fuzzy16
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fbdc105c-a0e2-45a2-a7bb-0778e23599c2 · outbound

This paper cites Federated machine learning: Con- cept and applications,.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Federated machine learning: Con- cept and applications,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:52:41.888375Z

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 b566547e-c0a9-41ea-9398-2ec08ec9ab43 · outbound

This paper cites Federated visual classification with real-world data distribution,.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Federated visual classification with real-world data distribution,

Reference 2

Resolution: verified exact; doi, observed 2026-08-07T00:52:38.939770Z

Source-reported events for the cited work

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Observation 205d0297-377b-476e-8259-665d2b86be15 · outbound

This paper cites Straggler-resilient federated learning: Leveraging the interplay between statistical accuracy and system heterogeneity,.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Straggler-resilient federated learning: Leveraging the interplay between statistical accuracy and system heterogeneity,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:52:41.879078Z

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Observation 1b9fd61f-9e37-4bd6-9609-936f5b74e16d · outbound

This paper cites Breaking barriers of system heterogeneity: Straggler-tolerant multimodal federated learning via knowledge distillation,.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Breaking barriers of system heterogeneity: Straggler-tolerant multimodal federated learning via knowledge distillation,

Reference 4

Resolution: verified exact; doi, observed 2026-08-07T00:52:38.791684Z

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Observation d7c6949e-7120-4dfc-b4d3-81ecde864630 · outbound

This paper cites Corrected with the Latest Version: Make Robust Asynchronous Federated Learning Possible.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Corrected with the Latest Version: Make Robust Asynchronous Federated Learning Possible

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-07T00:52:40.072580Z

Source-reported events for the cited work

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Observation 8bb5e928-fed6-4cee-9205-ceb5dce23005 · outbound

This paper cites Advances and Open Problems in Federated Learning.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Advances and Open Problems in Federated Learning

Reference 6

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Source-reported events for the cited work

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Observation 7e1649bd-5d6e-44c0-b29f-6a5566b92185 · outbound

This paper cites Fedsa: A staleness-aware asynchronous federated learning algorithm with non-iid data,.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Fedsa: A staleness-aware asynchronous federated learning algorithm with non-iid data,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:52:41.869911Z

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 8880e914-41cd-40bd-9547-1bd01b5fad18 · outbound

This paper cites Client Selection in Federated Learning: Principles, Challenges, and Opportunities.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Client Selection in Federated Learning: Principles, Challenges, and Opportunities

Reference 8

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Source-reported events for the cited work

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Observation 11b519b9-255d-4652-9eb0-d3e13e70bdd5 · outbound

This paper cites Fedmccs: Multicriteria client selection model for optimal iot federated learning,.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Fedmccs: Multicriteria client selection model for optimal iot federated learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:52:41.860297Z

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 584535e1-3275-479d-aa7b-68a912bf409d · outbound

This paper cites A sur- vey on federated learning systems: Vision, hype and reality for data privacy and protection,.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning A sur- vey on federated learning systems: Vision, hype and reality for data privacy and protection,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:52:41.849598Z

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 4f02aa53-f845-4b09-b9ba-068571058c35 · outbound

This paper cites Asynchronous federated optimization,.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Asynchronous federated optimization,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:52:41.839362Z

Source-reported events for the cited work

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Observation 125f3ba7-b2b8-4419-a3c0-85a59e9f63ed · outbound

This paper cites Communication-efficient federated deep learn- ing with layerwise asynchronous model update and temporally weighted ag- gregation,.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Communication-efficient federated deep learn- ing with layerwise asynchronous model update and temporally weighted ag- gregation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:52:41.800122Z

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 b96d358e-7501-438c-a4a3-a9aa94fe9163 · outbound

This paper cites Communication-Efficient Learning of Deep Networks from Decentralized Data,.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Communication-Efficient Learning of Deep Networks from Decentralized Data,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:52:41.664680Z

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 2767c8c1-8abf-42a2-8b6f-3a501ad8efaf · outbound

This paper cites Adaptive Federated Optimization.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Adaptive Federated Optimization

Reference 14

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Source-reported events for the cited work

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Observation 486ca158-8651-4c8f-b270-3ba74ca47521 · outbound

This paper cites Federated Optimization in Heterogeneous Networks.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Federated Optimization in Heterogeneous Networks

Reference 15

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Source-reported events for the cited work

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Observation d1ee85cc-c38a-4a66-a2fd-8efe462e872d · outbound

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

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning FedBN: Federated Learning on Non-IID Features via Local Batch Normalization

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T00:52:35.829723Z

Source-reported events for the cited work

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Observation 99eafc96-35f8-4307-9593-377536689645 · outbound

This paper cites Model-contrastive federated learning,.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Model-contrastive federated learning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:52:41.472172Z

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 1e2bf4e1-3573-421c-ae54-a320019745dd · outbound

This paper cites Fedasmu: Efficient asynchronous federated learning with dynamic staleness-aware model update,.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Fedasmu: Efficient asynchronous federated learning with dynamic staleness-aware model update,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:52:41.125661Z

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 ab833476-f15d-433b-bbae-af56aa568ef6 · outbound

This paper cites Tackling the data heterogeneity in asynchronous federated learning with cached update calibration,.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Tackling the data heterogeneity in asynchronous federated learning with cached update calibration,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:52:40.912227Z

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 e002ac8e-7ae3-4e25-9fc6-b6f57b6d9386 · outbound

This paper cites Federated learning with buffered asynchronous aggregation,.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Federated learning with buffered asynchronous aggregation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:52:40.834703Z

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 c8d84247-d7f0-4a34-88fd-5e15a4ad6de2 · outbound

This paper cites Fedfa: a fully asynchronous training paradigm for federated learning,.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Fedfa: a fully asynchronous training paradigm for federated learning,

Reference 21

Resolution: verified exact; doi, observed 2026-08-07T00:52:38.646619Z

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 0a81d082-fa95-479c-97b2-b11bffe8b7d8 · outbound

This paper cites Robust Federated Learning in a Heterogeneous Environment.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Robust Federated Learning in a Heterogeneous Environment

Reference 22

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Source-reported events for the cited work

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Observation b7ec34fc-ba83-43d9-a875-cc4e446a3a3c · outbound

This paper cites PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization

Reference 23

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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 b9ff245b-2d27-4c26-bf04-01c14854796b · outbound

This paper cites Clustered federated learn- ing: Model-agnostic distributed multitask optimization under privacy con- straints,.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Clustered federated learn- ing: Model-agnostic distributed multitask optimization under privacy con- straints,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:52:40.641014Z

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 48daa615-e7ce-47af-b283-86c775ca4994 · outbound

This paper cites An Efficient Framework for Clustered Federated Learning.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning An Efficient Framework for Clustered Federated Learning

Reference 25

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Source-reported events for the cited work

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Observation 98eff62c-e079-4ba9-a23c-b8c0baa905c4 · outbound

This paper cites Casa: Clustered federated learning with asynchronous clients,.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Casa: Clustered federated learning with asynchronous clients,

Reference 26

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Source-reported events for the cited work

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Observation 02d737a2-7c9c-4c6d-ae4b-7688697970bf · outbound

This paper cites Research on k-means clustering algorithm: An improved k-means clustering algorithm,.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Research on k-means clustering algorithm: An improved k-means clustering algorithm,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:52:40.490068Z

Source-reported events for the cited work

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Observation c8feab58-ab5a-4b60-9b8c-7a85a01f5059 · outbound

This paper cites Random projection in dimensionality reduction: applications to image and text data,.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Random projection in dimensionality reduction: applications to image and text data,

Reference 28

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Source-reported events for the cited work

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Observation e68ac903-43e0-4ed7-b179-7c92feecccee · outbound

This paper cites The Johnson-Lindenstrauss lemma is optimal for linear dimensionality reduction.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning The Johnson-Lindenstrauss lemma is optimal for linear dimensionality reduction

Reference 29

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Source-reported events for the cited work

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Observation fdf16627-b5ac-4b9e-9eb0-d3b36dc24b29 · outbound

This paper cites FLGo: A Fully Customizable Federated Learning Platform.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning FLGo: A Fully Customizable Federated Learning Platform

Reference 30

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Observation 8dfedd03-ccfd-4bc0-beb2-a5d30ce638dc · outbound

This paper cites Gradient-based learning applied to document recognition,.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Gradient-based learning applied to document recognition,

Reference 31

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Observation b1eb522b-8a9b-497f-9933-b20d13a7bce7 · outbound

This paper cites Mnist handwritten digit database,.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Mnist handwritten digit database,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:52:40.354615Z

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 1fa71948-1aa8-423a-9b27-5c9787bb6365 · outbound

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

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Learning multiple layers of features from tiny images,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:52:40.207140Z

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 bd45405b-f0a8-4b8b-83bc-6b17cedc1d39 · outbound

This paper cites Bag of Tricks for Efficient Text Classification.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Bag of Tricks for Efficient Text Classification

Reference 34

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Observation ae24f09e-6982-4a9c-ab98-f11c9ce9d718 · outbound

This paper cites Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,

Reference 35

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Source-reported events for the cited work

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Observation 8f3a80c1-e755-4a52-802c-2d2532da6506 · outbound

This paper cites Federated Learning Based on Dynamic Regularization.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Federated Learning Based on Dynamic Regularization

Reference 36

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Source-reported events for the cited work

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Observation 40fe91fa-0e86-47d8-8a57-248ca775f001 · outbound

This paper cites Asynchronous Federated Optimization.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Asynchronous Federated Optimization

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T00:52:35.486130Z

Source-reported events for the cited work

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Observation 93a26c0e-6016-40e5-9fec-9ac263642317 · outbound

This paper cites Federated Learning with Buffered Asynchronous Aggregation.

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning Federated Learning with Buffered Asynchronous Aggregation

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T00:52:36.209470Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.