Paper Citation Record · LEDGER

Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks

As of 15 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2506.12425.

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

pith.paper-citation-record.v1
2506.12425 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:55:27.584622Z

measured 15 of 15 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 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

15 of 15 outbound references displayed

verified exact0
verified fuzzy14
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 937a8679-ba0a-4dc7-a940-40d9ef135428 · outbound

This paper cites Personalized subgraph federated learning.

Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks Personalized subgraph federated learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:55:30.097608Z

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.

Observation e1cbbf90-19e2-4fec-a483-e1d8cd646f5b · outbound

This paper cites Flotilla: A scalable, modular and resilient federated learning framework for heterogeneous resources.Journal of Parallel and Distributed Computing, 203:105103, 2025.

Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks Flotilla: A scalable, modular and resilient federated learning framework for heterogeneous resources.Journal of Parallel and Distributed Computing, 203:105103, 2025

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:55:29.891111Z

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.

Observation 237dd0b4-367f-4575-8b0c-4815d07ff70b · outbound

This paper cites Tifl: A tier-based federated learning system.

Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks Tifl: A tier-based federated learning system

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:55:29.668259Z

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.

Observation ecc83786-b295-4dda-8707-32278f89320a · outbound

This paper cites Inductive representation learning on large graphs.Advances in Neural Information Processing Sys- tems, 2017.

Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks Inductive representation learning on large graphs.Advances in Neural Information Processing Sys- tems, 2017

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:55:29.402153Z

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.

Observation 62f2a6a2-650c-487b-a0c0-637eef511f1f · outbound

This paper cites METIS: A software package for par- titioning unstructured graphs, partitioning meshes, and computing fill- reducing orderings of sparse matrices, 1997.

Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks METIS: A software package for par- titioning unstructured graphs, partitioning meshes, and computing fill- reducing orderings of sparse matrices, 1997

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:55:29.194040Z

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.

Observation 4ecf5a3a-96a1-4274-8be2-233ea4d25b50 · outbound

This paper cites Semi-supervised classification with graph convolutional networks.ArXiv, 2016.

Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks Semi-supervised classification with graph convolutional networks.ArXiv, 2016

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:55:29.056395Z

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.

Observation fc7fa35c-a502-42a1-9d38-8210b2279dca · outbound

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

Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks Communication-efficient learning of deep networks from decentralized data

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:55:28.897196Z

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.

Observation 9ad4b378-f0b5-4ff3-9c21-ee4c1a230432 · outbound

This paper cites Weisfeiler and leman go neural: Higher-order graph neural networks.

Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks Weisfeiler and leman go neural: Higher-order graph neural networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:55:28.705670Z

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.

Observation 826c152b-00e4-467d-8461-f1168781fa5f · outbound

This paper cites Deep graph library: Towards efficient and scalable deep learning on graphs.

Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks Deep graph library: Towards efficient and scalable deep learning on graphs

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:55:28.534493Z

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.

Observation 33ca4104-fe96-4694-a867-4aeedf7b221f · outbound

This paper cites Federatedscope-gnn: Towards a unified, com- prehensive and efficient package for federated graph learning.

Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks Federatedscope-gnn: Towards a unified, com- prehensive and efficient package for federated graph learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:55:28.416662Z

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.

Observation 172c31b4-e7c5-47ef-a2a9-1c8e7afb358c · outbound

This paper cites FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation.

Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T00:55:27.296486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60136711-02de-491e-9a5b-47075b2b3ace · outbound

This paper cites Embedding communication for federated graph neural networks with privacy guarantees.

Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks Embedding communication for federated graph neural networks with privacy guarantees

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:55:28.258597Z

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.

Observation 22a96243-a8ad-4b55-9767-1894835857ba · outbound

This paper cites Federated graph classification overnon-iidgraphs.

Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks Federated graph classification overnon-iidgraphs

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:55:28.089380Z

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.

Observation f119ec63-118a-4542-bcf7-ef35e8dd42eb · outbound

This paper cites Fedgcn: Convergence-communication tradeoffs in federated training of graph convo- lutional networks.

Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks Fedgcn: Convergence-communication tradeoffs in federated training of graph convo- lutional networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:55:27.941989Z

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.

Observation 34a717c1-332a-40db-9094-a80918ffe5f6 · outbound

This paper cites Sub- graph federated learning with missing neighbor generation.

Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks Sub- graph federated learning with missing neighbor generation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:55:27.776944Z

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.

Pith citing papers

No inbound Pith citation observations are available.