Paper Citation Record · LEDGER

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor

As of 18 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 2 inbound Pith citation observations for arXiv:2412.18355.

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

pith.paper-citation-record.v1
2412.18355 v2

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T04:49:47.360884Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:46:50.472654Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-18T21:16:51.364844Z

Reference resolution

44 of 44 outbound references displayed

verified exact1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cd710827-23ae-41c7-bad7-9751e122b751 · outbound

This paper cites A data-free ap- proach to mitigate catastrophic forgetting in federated class incremental learning for vision tasks.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor A data-free ap- proach to mitigate catastrophic forgetting in federated class incremental learning for vision tasks

Reference 1

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Observation cf6ccafb-d1ac-4251-ba89-5490bc0e37cf · outbound

This paper cites Feder- ated learning with client subsampling, data heterogeneity, and unbounded smoothness: A new algorithm and lower bounds.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Feder- ated learning with client subsampling, data heterogeneity, and unbounded smoothness: A new algorithm and lower bounds

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:49:47.706228Z

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Observation 577e85ad-bf27-4e3c-ac51-8a6407836cfc · outbound

This paper cites Federated class-incremental learn- ing.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Federated class-incremental learn- ing

Reference 3

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Observation 1b4f19c5-a29e-4526-a12b-e85a89dbc5b8 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 4

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Observation b9d54cfa-5472-48e7-896a-56a15b14130c · outbound

This paper cites Continual learning with transformers for image classification.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Continual learning with transformers for image classification

Reference 5

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Observation 229ac1f7-389f-4dc5-9442-28988620ecad · outbound

This paper cites Ten Challenging Problems in Federated Foundation Models.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Ten Challenging Problems in Federated Foundation Models

Reference 6

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Observation f66bbaf4-45a0-4728-b331-df31a94641b2 · outbound

This paper cites Fed- prok: Trustworthy federated class-incremental learning via prototypical feature knowledge transfer.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Fed- prok: Trustworthy federated class-incremental learning via prototypical feature knowledge transfer

Reference 7

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Observation 5cb1ee3f-38e4-473e-9f65-577a948666cd · outbound

This paper cites Embracing change: Continual learning in deep neural networks.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Embracing change: Continual learning in deep neural networks

Reference 8

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Observation ad82eef3-25c0-4be9-9e42-6ef3a1b28b54 · outbound

This paper cites The many faces of robust- ness: A critical analysis of out-of-distribution generalization.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor The many faces of robust- ness: A critical analysis of out-of-distribution generalization

Reference 9

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Observation d7737d55-9423-44ac-a790-b33bba27605a · outbound

This paper cites Learn from others and be yourself in heterogeneous federated learning.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Learn from others and be yourself in heterogeneous federated learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:49:47.659791Z

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Observation 2bd8efa3-77a7-4248-a341-3faef34e3153 · outbound

This paper cites Grounding Foundation Models through Federated Transfer Learning: A General Framework.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Grounding Foundation Models through Federated Transfer Learning: A General Framework

Reference 11

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Observation 1d22cc2b-275b-47ef-848a-84e470d8931f · outbound

This paper cites Navigating data heterogeneity in fed- erated learning: a semi-supervised federated object detec- tion.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Navigating data heterogeneity in fed- erated learning: a semi-supervised federated object detec- tion

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:49:47.651949Z

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Observation a985025b-bef1-4eb2-a503-16d909e7417b · outbound

This paper cites Overcoming catastrophic forgetting in neu- ral networks.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Overcoming catastrophic forgetting in neu- ral networks

Reference 13

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Observation 4c9a6563-3aae-4f22-8d2e-880f6570c062 · outbound

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

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Learning multiple layers of features from tiny images

Reference 14

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Observation 9e36c7dc-7317-43db-b159-91f0a10d26bb · outbound

This paper cites Fac- ing spatiotemporal heterogeneity: A unified federated con- tinual learning framework with self-challenge rehearsal for industrial monitoring tasks.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Fac- ing spatiotemporal heterogeneity: A unified federated con- tinual learning framework with self-challenge rehearsal for industrial monitoring tasks

Reference 15

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Source-reported events for the cited work

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Observation e14f8e64-43f6-464d-8e42-5462923048d9 · outbound

This paper cites Fed- erated learning on non-iid data silos: An experimental study.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Fed- erated learning on non-iid data silos: An experimental study

Reference 16

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Source-reported events for the cited work

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Observation 96cfbe94-13b1-4217-a20d-b13119919772 · outbound

This paper cites Federated optimiza- tion in heterogeneous networks.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Federated optimiza- tion in heterogeneous networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:49:47.614764Z

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Observation 56e9860d-1d2d-4a6c-9a07-0df39631ea40 · outbound

This paper cites Towards efficient re- play in federated incremental learning.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Towards efficient re- play in federated incremental learning

Reference 18

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Observation c842e4fe-eba5-4dda-a991-9349deede327 · outbound

This paper cites Unleashing the power of continual learning on non-centralized devices: A survey,.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Unleashing the power of continual learning on non-centralized devices: A survey,

Reference 19

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Observation 571d04e0-8930-473b-9623-012a56dfbbba · outbound

This paper cites Sr-fdil: Synergistic replay for fed- erated domain-incremental learning.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Sr-fdil: Synergistic replay for fed- erated domain-incremental learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:49:47.589997Z

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Observation 57c91b7a-a7a7-4f83-8b14-ffda107cbf8a · outbound

This paper cites Learning to prompt knowledge transfer for open-world continual learning.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Learning to prompt knowledge transfer for open-world continual learning

Reference 21

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Observation 5865fe3b-84b4-4239-9d0b-3f57e8d0fff5 · outbound

This paper cites Personalized federated domain- incremental learning based on adaptive knowledge match- ing.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Personalized federated domain- incremental learning based on adaptive knowledge match- ing

Reference 22

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Observation 27aa7852-5c65-4da3-bf8f-21249e7ec31e · outbound

This paper cites Learning without forgetting.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Learning without forgetting

Reference 23

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Observation 91b98dd3-43cb-436c-93a7-ca192b84384b · outbound

This paper cites Diffusion-driven data replay: A novel approach to combat forgetting in federated class continual learning.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Diffusion-driven data replay: A novel approach to combat forgetting in federated class continual learning

Reference 24

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Observation 2447b34c-3ecc-4a93-a21d-cf880b8eee30 · outbound

This paper cites Continual federated learning based on knowledge dis- tillation.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Continual federated learning based on knowledge dis- tillation

Reference 25

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Observation 6ce8897c-6643-4e83-8ec7-d095d1bf1a11 · outbound

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

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Communication- efficient learning of deep networks from decentralized data

Reference 26

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Observation d9ca37f5-243f-49ec-baff-42bea16f205b · outbound

This paper cites Towards exemplar-free continual learning in vision transformers: an account of at- tention, functional and weight regularization.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Towards exemplar-free continual learning in vision transformers: an account of at- tention, functional and weight regularization

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:49:47.535187Z

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Observation 9d8420a5-561c-4662-b504-943e4181a35f · outbound

This paper cites Handling data heterogeneity via architectural de- sign for federated visual recognition.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Handling data heterogeneity via architectural de- sign for federated visual recognition

Reference 28

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Observation f76de2a2-db9b-4d7b-88b7-ec147bbe2548 · outbound

This paper cites Learning representations by back-propagating er- rors.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Learning representations by back-propagating er- rors

Reference 29

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Observation 48b73aeb-a96a-4986-8ce2-b3af1922bd49 · outbound

This paper cites A closer look at rehearsal-free continual learning.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor A closer look at rehearsal-free continual learning

Reference 30

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Observation 5ca66e6e-0a7e-4fb6-a5b1-485b9a07b4b2 · outbound

This paper cites Tackling the objective inconsistency prob- lem in heterogeneous federated optimization.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Tackling the objective inconsistency prob- lem in heterogeneous federated optimization

Reference 31

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Observation 18bdc763-a528-408b-89ba-c9f9c1cf9c81 · outbound

This paper cites Continual learning with lifelong vision trans- former.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Continual learning with lifelong vision trans- former

Reference 32

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Observation 4ca9dd81-e998-4d29-8cd3-d4b17af6a0b7 · outbound

This paper cites Dualprompt: Complementary prompting for rehearsal-free continual learning.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Dualprompt: Complementary prompting for rehearsal-free continual learning

Reference 33

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Observation b7501b5e-02d7-456c-a8a7-d9d842fb9e51 · outbound

This paper cites Age-aware data selec- tion and aggregator placement for timely federated continual learning in mobile edge computing.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Age-aware data selec- tion and aggregator placement for timely federated continual learning in mobile edge computing

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:49:47.486746Z

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Observation 12f87ddc-00ce-403f-a656-1380827d407d · outbound

This paper cites Federated continual learning via knowledge fu- sion: A survey.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Federated continual learning via knowledge fu- sion: A survey

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:49:47.479198Z

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Observation cc7b3879-9581-4681-a8ec-26944219e6a6 · outbound

This paper cites Fedfed: Feature distilla- tion against data heterogeneity in federated learning.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Fedfed: Feature distilla- tion against data heterogeneity in federated learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:49:47.471656Z

Source-reported events for the cited work

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Observation 950800a8-3431-4cf6-885c-db5085f02951 · outbound

This paper cites Federated continual learning with weighted inter-client transfer.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Federated continual learning with weighted inter-client transfer

Reference 37

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Observation 483b8115-33d5-4ad8-ad15-57a5cbb26d98 · outbound

This paper cites Overcoming spatial-temporal catas- trophic forgetting for federated class-incremental learning.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Overcoming spatial-temporal catas- trophic forgetting for federated class-incremental learning

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:49:47.454889Z

Source-reported events for the cited work

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Observation 1b6847ad-5731-4f7d-86f3-71264db78cac · outbound

This paper cites Personalized federated continual learning via multi-granularity prompt.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Personalized federated continual learning via multi-granularity prompt

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:49:47.446259Z

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Observation 8eb89dff-b5fd-4f2a-a1f3-e01f23aef89d · outbound

This paper cites Target: Federated class-continual learning via exemplar-free distillation.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Target: Federated class-continual learning via exemplar-free distillation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:49:47.438573Z

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Observation 8cc8d46a-0eec-4bff-b70d-4863b3ada5b7 · outbound

This paper cites Cross-fcl: Toward a cross-edge federated contin- ual learning framework in mobile edge computing systems.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Cross-fcl: Toward a cross-edge federated contin- ual learning framework in mobile edge computing systems

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:49:47.430925Z

Source-reported events for the cited work

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Observation ec8cb6a9-e1f2-4c6f-bc7f-098eb938c176 · outbound

This paper cites Federated Learning with Non-IID Data.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Federated Learning with Non-IID Data

Reference 42

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Observation b79918ca-e02f-4811-a79e-cba90fa0a3fb · outbound

This paper cites Flee: A hierarchical federated learning framework for distributed deep neural network over cloud, edge, and end device.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Flee: A hierarchical federated learning framework for distributed deep neural network over cloud, edge, and end device

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:49:47.423229Z

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Observation d4b4818e-f751-49e8-8b20-efa4721ee857 · outbound

This paper cites Semi-hfl: semi-supervised federated learning for heterogeneous devices.

Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor Semi-hfl: semi-supervised federated learning for heterogeneous devices

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:49:47.414964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation 942c9e10-78e1-4f0c-9dc8-f2107beae059 · inbound

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey cites this paper.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor

Reference 135

Resolution: unresolved; no resolver link, observed 2026-08-11T12:46:50.472654Z

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Observation ebdfdaac-8c84-4839-b21c-68978a383c90 · inbound

BRAIN: Bias-Mitigation Continual Learning Approach to Vision-Brain Understanding cites this paper.

BRAIN: Bias-Mitigation Continual Learning Approach to Vision-Brain Understanding Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor

Reference 88

Resolution: verified exact; arxiv_id, observed 2026-05-18T21:16:51.366999Z

Source-reported events for the cited work

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