Paper Citation Record · LEDGER

Distilling A Universal Expert from Clustered Federated Learning

As of 21 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2506.20285.

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

pith.paper-citation-record.v1
2506.20285 v1

Coverage vector

measured 40 of 40 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-06T22:59:17.446050Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

40 of 40 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8687a53b-0a8c-4d4e-9855-33111f3979ab · outbound

This paper cites Federated Learning Based on Dynamic Regularization.

Distilling A Universal Expert from Clustered Federated Learning Federated Learning Based on Dynamic Regularization

Reference 1

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Observation d39dfd85-19bd-4891-ae0e-246f9913d0c4 · outbound

This paper cites Density-based spatial cluster- ing of applications with noise.

Distilling A Universal Expert from Clustered Federated Learning Density-based spatial cluster- ing of applications with noise

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:20.096164Z

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Observation 850c8d95-9b9c-4646-8eb7-bd47de81dfd3 · outbound

This paper cites Data-free ensemble knowledge distillation for privacy-conscious multimedia model com- pression.

Distilling A Universal Expert from Clustered Federated Learning Data-free ensemble knowledge distillation for privacy-conscious multimedia model com- pression

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:19.993325Z

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Observation 5fe0ef16-9fa6-4ab3-ba95-23bdb6ceeeee · outbound

This paper cites Deep residual learning for image recog- nition.

Distilling A Universal Expert from Clustered Federated Learning Deep residual learning for image recog- nition

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:19.971542Z

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Observation e978f4b6-62df-49df-bf0a-45b517ba6c68 · outbound

This paper cites Swiftagg: Communication-efficient and dropout-resistant secure aggregation for federated learning with worst-case security guarantees.

Distilling A Universal Expert from Clustered Federated Learning Swiftagg: Communication-efficient and dropout-resistant secure aggregation for federated learning with worst-case security guarantees

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:19.926385Z

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Observation e637144b-4053-4bb0-a086-1e2c3e39645c · outbound

This paper cites Clustered federated learning via gradient- based partitioning.

Distilling A Universal Expert from Clustered Federated Learning Clustered federated learning via gradient- based partitioning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:19.875698Z

Source-reported events for the cited work

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Observation 9d8862e9-a265-429a-8ab0-cb5d2bb693a0 · outbound

This paper cites Learning multiple layers of features from tiny im- ages.

Distilling A Universal Expert from Clustered Federated Learning Learning multiple layers of features from tiny im- ages

Reference 16

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Source-reported events for the cited work

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Observation a7665740-3eac-4d5d-9419-8012ede1e585 · outbound

This paper cites Model-contrastive federated learning.

Distilling A Universal Expert from Clustered Federated Learning Model-contrastive federated learning

Reference 18

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Source-reported events for the cited work

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Observation 990c494c-6d60-44a5-ac45-a145f1a8aa31 · outbound

This paper cites Casa: Clustered fed- erated learning with asynchronous clients.

Distilling A Universal Expert from Clustered Federated Learning Casa: Clustered fed- erated learning with asynchronous clients

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:19.821116Z

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Observation 1fc1ea44-bea7-4308-8c71-cdd8008ade38 · outbound

This paper cites Multi-center federated learning: clients clustering for better personal- ization.World Wide Web, 26(1):481–500,.

Distilling A Universal Expert from Clustered Federated Learning Multi-center federated learning: clients clustering for better personal- ization.World Wide Web, 26(1):481–500,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:19.799312Z

Source-reported events for the cited work

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Observation cebbd7f2-6874-44b8-9f50-e0245c5d242b · outbound

This paper cites Toward efficient and privacy- preserving computing in big data era.IEEE Network, 28(4):46–50,.

Distilling A Universal Expert from Clustered Federated Learning Toward efficient and privacy- preserving computing in big data era.IEEE Network, 28(4):46–50,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:19.777789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0d1be58f-8891-4114-a392-9d9d1601970d · outbound

This paper cites Structured federated learning through clustered additive modeling.Advances in Neural Information Processing Systems, 36:43097–43107,.

Distilling A Universal Expert from Clustered Federated Learning Structured federated learning through clustered additive modeling.Advances in Neural Information Processing Systems, 36:43097–43107,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:19.758299Z

Source-reported events for the cited work

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Observation d85d1ea4-141d-4458-8e09-88405b3f96ba · outbound

This paper cites hdbscan: Hierarchical density based clus- tering.J.

Distilling A Universal Expert from Clustered Federated Learning hdbscan: Hierarchical density based clus- tering.J

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:19.741751Z

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Observation 743f2359-bc97-4736-9eea-f5ab422c9f1e · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

Distilling A Universal Expert from Clustered Federated Learning Reading digits in natural images with unsupervised feature learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:19.713293Z

Source-reported events for the cited work

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Observation 06a9927b-0e6c-4038-b319-30349813d8c3 · outbound

This paper cites Fedsoft: Soft clustered federated learning with proximal local updating,.

Distilling A Universal Expert from Clustered Federated Learning Fedsoft: Soft clustered federated learning with proximal local updating,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:19.677101Z

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Observation 7a2fe9dc-c7f8-4200-b56e-ccceb8530910 · outbound

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Distilling A Universal Expert from Clustered Federated Learning Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:59:19.658248Z

Source-reported events for the cited work

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Observation 019229aa-692a-42a6-a815-09a666db174f · outbound

This paper cites Teacher as a lenient expert: Teacher-agnostic data-free knowledge distillation.

Distilling A Universal Expert from Clustered Federated Learning Teacher as a lenient expert: Teacher-agnostic data-free knowledge distillation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:19.588019Z

Source-reported events for the cited work

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Observation ea95d85b-62e2-46d2-a4d3-6bd8443ef300 · outbound

This paper cites Turbo-aggregate: Breaking the quadratic ag- gregation barrier in secure federated learning.IEEE Jour- nal on Selected Areas in Information Theory, 2(1):479– 489,.

Distilling A Universal Expert from Clustered Federated Learning Turbo-aggregate: Breaking the quadratic ag- gregation barrier in secure federated learning.IEEE Jour- nal on Selected Areas in Information Theory, 2(1):479– 489,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:19.314734Z

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Observation 1968bf9f-3fc0-4685-8f18-a6835248bb64 · outbound

This paper cites Entrocfl: Entropy-based clustered federated learning with incentive mechanism.IEEE Internet of Things Journal, 12(1):986–1001,.

Distilling A Universal Expert from Clustered Federated Learning Entrocfl: Entropy-based clustered federated learning with incentive mechanism.IEEE Internet of Things Journal, 12(1):986–1001,

Reference 32

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Observation 65c9f343-b33d-4e62-a4d6-d90510f877ac · outbound

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Distilling A Universal Expert from Clustered Federated Learning Unresolved cited work

Reference 34

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Observation f860f45f-367d-415d-91d5-f7906b6f11ce · outbound

This paper cites Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learning.

Distilling A Universal Expert from Clustered Federated Learning Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learning

Reference 35

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Observation 9ec5bca9-6230-4694-b060-380f905028c6 · outbound

This paper cites Data-free knowledge amalga- mation via group-stack dual-gan.

Distilling A Universal Expert from Clustered Federated Learning Data-free knowledge amalga- mation via group-stack dual-gan

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:18.587822Z

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Observation 07f1fe16-f4e2-44e3-99dd-f34567f8a200 · outbound

This paper cites Knowledge extraction with no observable data.Advances in Neural Information Processing Systems, 32,.

Distilling A Universal Expert from Clustered Federated Learning Knowledge extraction with no observable data.Advances in Neural Information Processing Systems, 32,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:18.373534Z

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Observation 9dca0b14-abf5-40aa-9a47-817400f4f3ea · outbound

This paper cites Fine-tuning global model via data-free knowledge distillation for non-iid fed- erated learning.

Distilling A Universal Expert from Clustered Federated Learning Fine-tuning global model via data-free knowledge distillation for non-iid fed- erated learning

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:18.175315Z

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Observation 204d507e-bb8c-473b-b88a-14c1e731fa9a · outbound

This paper cites Dual personalization on federated recommen- dation.

Distilling A Universal Expert from Clustered Federated Learning Dual personalization on federated recommen- dation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:17.994826Z

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Observation 9d89b334-cab0-46a5-8516-c2b50413ac0e · outbound

This paper cites Data-free knowledge distillation for heterogeneous federated learning.

Distilling A Universal Expert from Clustered Federated Learning Data-free knowledge distillation for heterogeneous federated learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:17.824019Z

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Observation c621a500-d4ce-47ed-94dd-ac9321f46096 · outbound

This paper cites Taking advantage of the mistakes: Rethinking clustered federated learning for iot anomaly detection.IEEE Transactions on Parallel and Distributed Systems, 35(6):862–876,.

Distilling A Universal Expert from Clustered Federated Learning Taking advantage of the mistakes: Rethinking clustered federated learning for iot anomaly detection.IEEE Transactions on Parallel and Distributed Systems, 35(6):862–876,

Reference 1996

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:20.077112Z

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Observation 35e2125d-851b-40e9-b1d4-fb2485aa735b · outbound

This paper cites An efficient framework for clustered federated learning.Advances in Neural In- formation Processing Systems, 33:19586–19597,.

Distilling A Universal Expert from Clustered Federated Learning An efficient framework for clustered federated learning.Advances in Neural In- formation Processing Systems, 33:19586–19597,

Reference 2007

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:20.036227Z

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Observation db9dad69-ae19-4c1d-ace9-a011ecfacb0f · outbound

This paper cites Federated optimization in heterogeneous networks.Pro- ceedings of Machine learning and systems, 2:429–450,.

Distilling A Universal Expert from Clustered Federated Learning Federated optimization in heterogeneous networks.Pro- ceedings of Machine learning and systems, 2:429–450,

Reference 2009

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Observation 3d78230a-8274-453b-92a9-e054dd2ae077 · outbound

This paper cites Federated learning for internet of things: A comprehensive survey.IEEE Communications Surveys & Tutorials, 23(3):1622–1658,.

Distilling A Universal Expert from Clustered Federated Learning Federated learning for internet of things: A comprehensive survey.IEEE Communications Surveys & Tutorials, 23(3):1622–1658,

Reference 2011

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:19.695071Z

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Observation 2e96ca16-7842-4dbc-bc99-275b2e9f8b9b · outbound

This paper cites On the Convergence of Clustered Federated Learning.

Distilling A Universal Expert from Clustered Federated Learning On the Convergence of Clustered Federated Learning

Reference 2014

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Source-reported events for the cited work

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Observation 28fc0e46-c559-4790-ba45-0e1917e30c86 · outbound

This paper cites Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification.

Distilling A Universal Expert from Clustered Federated Learning Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-06T22:59:14.448347Z

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Observation 46f541e3-70f5-4fac-8f11-5cbd95fa358f · outbound

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

Distilling A Universal Expert from Clustered Federated Learning Communication-efficient learning of deep networks from decentralized data

Reference 2017

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Observation 9a38de41-4130-407b-8b00-fb15c672ab23 · outbound

This paper cites Active client selection for clustered feder- ated learning.IEEE Transactions on Neural Networks and Learning Systems, 35(11):16424–16438,.

Distilling A Universal Expert from Clustered Federated Learning Active client selection for clustered feder- ated learning.IEEE Transactions on Neural Networks and Learning Systems, 35(11):16424–16438,

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:19.949900Z

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Observation 2f54595d-4fc7-4a33-8323-54e72ac75953 · outbound

This paper cites Fedrc: Tackling diverse distribution shifts challenge in federated learning by robust clustering,.

Distilling A Universal Expert from Clustered Federated Learning Fedrc: Tackling diverse distribution shifts challenge in federated learning by robust clustering,

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:20.015530Z

Source-reported events for the cited work

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Observation b3606672-fc64-4e0d-9dc1-8c9aa97e5cb0 · outbound

This paper cites Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges.IEEE Communications Surveys & Tutorials, 25(4):2983–3013,.

Distilling A Universal Expert from Clustered Federated Learning Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges.IEEE Communications Surveys & Tutorials, 25(4):2983–3013,

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:20.141402Z

Source-reported events for the cited work

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Observation 4f0e8329-29b8-406f-a8c7-a212847eeb43 · outbound

This paper cites Scaffold: Stochastic controlled averaging for federated learning.

Distilling A Universal Expert from Clustered Federated Learning Scaffold: Stochastic controlled averaging for federated learning

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:19.902708Z

Source-reported events for the cited work

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Observation 27a46bf7-4e5b-4b71-b6b8-922e66e19299 · outbound

This paper cites Flexible clustered federated learning for client- level data distribution shift.IEEE Transactions on Parallel and Distributed Systems, 33(11):2661–2674,.

Distilling A Universal Expert from Clustered Federated Learning Flexible clustered federated learning for client- level data distribution shift.IEEE Transactions on Parallel and Distributed Systems, 33(11):2661–2674,

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:20.117637Z

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Observation 5b2c953b-2cbd-449b-8cbd-7d0404564d9b · outbound

This paper cites Clustering by passing messages between data points.sci- ence, 315(5814):972–976,.

Distilling A Universal Expert from Clustered Federated Learning Clustering by passing messages between data points.sci- ence, 315(5814):972–976,

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:20.055418Z

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Observation dda92ae8-2fae-4b84-b341-f50167168901 · outbound

This paper cites an unresolved cited work.

Distilling A Universal Expert from Clustered Federated Learning Unresolved cited work

Reference 2025

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:59:18.962572Z

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Pith citing papers

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