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Paper Citation Record · LEDGER

Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2204.12703.

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

pith.paper-citation-record.v1
2204.12703 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:59:34.389977Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T01:06:24.110193Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e0b50cb5-274e-43e7-8a67-0e34baf24910 · inbound

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data cites this paper.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T13:59:34.389977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:59:34.389977Z digest=sha256:51cdd3ee3cf765f11407065d4b802e5cbb3efd385e9290ca3196b147311fc897

Observation 2727c497-b521-446f-b96b-c9f4a4c99801 · inbound

Representation-Aligned Multi-Scale Personalization for Federated Learning cites this paper.

Representation-Aligned Multi-Scale Personalization for Federated Learning Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning

Reference 4

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T09:56:01.499239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-10T15:45:17.555896Z digest=sha256:1dc257850894ca8a5fb6e1a49e8dacdd8f77ec2ed91a7dd21c86000ed25bad5a

Observation bb9d0930-6404-44cc-b733-c3b883867141 · inbound

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion cites this paper.

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning

Reference 81

Resolution
verified exact
arxiv_id, observed 2026-05-11T12:56:06.666402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-05-10T02:35:40.593397Z digest=sha256:b82aa8d50ae52ead6d5a05b4e661b47fa5fcd8e97c76cfdbb9ca6d28937fc7d0

Observation 0dd280a6-d5bb-4e5b-b324-02c9a844ecda · inbound

Boosting Multimodal Federated Learning via Chained Modality Optimization cites this paper.

Boosting Multimodal Federated Learning via Chained Modality Optimization Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning

Reference 2

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T01:06:24.111732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T12:50:41.988694Z digest=sha256:36f60f365e0857f59a76b2c76046c68834c01cb9e1b56cefa8fbb907b31ae6e8