Paper Citation Record · LEDGER

Clustered Federated Learning via Embedding Distributions

As of 8 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 1 inbound Pith citation observation for arXiv:2506.07769.

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

pith.paper-citation-record.v1
2506.07769 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:32:23.085731Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T14:08:24.900057Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T14:10:28.759826Z

Reference resolution

17 of 17 outbound references displayed

verified exact2
verified fuzzy4
unresolved10
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 003e81cb-4aed-4af4-90aa-f8750c9e33dd · outbound

This paper cites We use Python Optimal Transport 0.9.5 [Flamary et al., 2021] for the EMD calculations.

Clustered Federated Learning via Embedding Distributions We use Python Optimal Transport 0.9.5 [Flamary et al., 2021] for the EMD calculations

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:23.365030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d5979e8e-2e37-4ae5-94fb-cbdcf866c128 · outbound

This paper cites Federated Optimization: Distributed Machine Learning for On-Device Intelligence.

Clustered Federated Learning via Embedding Distributions Federated Optimization: Distributed Machine Learning for On-Device Intelligence

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T05:32:23.035507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a06aff79-d3c4-42ed-b00a-ee826ef61889 · outbound

This paper cites Theoretical analysis of domain adaptation with optimal transport.

Clustered Federated Learning via Embedding Distributions Theoretical analysis of domain adaptation with optimal transport

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:23.419235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 46132dba-45d6-4f7e-aba2-294f52731bb8 · outbound

This paper cites an unresolved cited work.

Clustered Federated Learning via Embedding Distributions Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:32:23.397263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 493be9af-6361-4a2a-854a-105d6d3cecf0 · outbound

This paper cites In addition to the clustering identification approach, another point of distinction is if the clustering is soft or hard.

Clustered Federated Learning via Embedding Distributions In addition to the clustering identification approach, another point of distinction is if the clustering is soft or hard

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:23.381804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 86ccbe82-bcb4-42a7-803d-3d0b4bef5915 · outbound

This paper cites FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering.

Clustered Federated Learning via Embedding Distributions FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering

Reference 1985

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:32:23.271541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3d2a6a78-967c-44c5-8dff-683049890d8f · outbound

This paper cites Intriguing properties of neural networks.

Clustered Federated Learning via Embedding Distributions Intriguing properties of neural networks

Reference 2004

Resolution: unresolved; no resolver link, observed 2026-08-07T05:32:23.060257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a897e71-cf47-467d-b647-4cac2bea47e7 · outbound

This paper cites Three Approaches for Personalization with Applications to Federated Learning.

Clustered Federated Learning via Embedding Distributions Three Approaches for Personalization with Applications to Federated Learning

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-07T05:32:23.045514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f843210-cbc7-48ef-a7c6-a32322577efa · outbound

This paper cites Towards Federated Learning at Scale: System Design.

Clustered Federated Learning via Embedding Distributions Towards Federated Learning at Scale: System Design

Reference 2010

Resolution: unresolved; no resolver link, observed 2026-08-07T05:32:23.005299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f4b807b-04a8-4f38-9668-ab408876afe5 · outbound

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

Clustered Federated Learning via Embedding Distributions An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-07T05:32:23.010816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79cd4bc5-aed3-4399-90b7-50c5a54dec91 · outbound

This paper cites Federated Adversarial Domain Adaptation.

Clustered Federated Learning via Embedding Distributions Federated Adversarial Domain Adaptation

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T05:32:23.050411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 872fe15f-0a92-4d85-b026-9525eea9a335 · outbound

This paper cites Balancing Similarity and Complementarity for Federated Learning.

Clustered Federated Learning via Embedding Distributions Balancing Similarity and Complementarity for Federated Learning

Reference 2018

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:32:23.133653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a33d1900-6bca-44c4-8e06-6175c260e847 · outbound

This paper cites FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering.

Clustered Federated Learning via Embedding Distributions FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T05:32:23.015651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2714bd9f-207d-4a5e-a503-b89e00b05b4c · outbound

This paper cites PACFL and FedClust require a threshold similar to our ϵ which we tune to find the closest to the optimal split in the first epoch.

Clustered Federated Learning via Embedding Distributions PACFL and FedClust require a threshold similar to our ϵ which we tune to find the closest to the optimal split in the first epoch

Reference 2020

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T05:32:23.344306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 321260aa-a652-4f49-82af-6c06d5d4c234 · outbound

This paper cites Privacy via the Johnson-Lindenstrauss Transform.

Clustered Federated Learning via Embedding Distributions Privacy via the Johnson-Lindenstrauss Transform

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T05:32:23.030649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2df69bac-029e-42b8-a26c-3ad6bd9e8518 · outbound

This paper cites Domain Adaptation: Learning Bounds and Algorithms.

Clustered Federated Learning via Embedding Distributions Domain Adaptation: Learning Bounds and Algorithms

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T05:32:23.040252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73e032c2-66b6-4b46-89ff-39cd3e0635c6 · outbound

This paper cites Extensions of lipshitz mapping into hilbert space.

Clustered Federated Learning via Embedding Distributions Extensions of lipshitz mapping into hilbert space

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:23.437814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation 6895a04e-22bb-4f34-91ca-e4f50b4b12a2 · inbound

Joint Clustering and Prediction of the Quality of Service in Vehicular Cellular Networks cites this paper.

Joint Clustering and Prediction of the Quality of Service in Vehicular Cellular Networks Clustered Federated Learning via Embedding Distributions

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-10T14:10:28.761558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.