Paper Citation Record · LEDGER

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning

As of 22 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2506.21054.

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

pith.paper-citation-record.v1
2506.21054 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:40:57.815989Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

37 of 37 outbound references displayed

verified exact1
verified fuzzy31
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7d697382-fb9e-4135-b1ba-adc0e11c8ad2 · outbound

This paper cites Learning under concept drift: A review.IEEE Trans.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Learning under concept drift: A review.IEEE Trans

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:05.002215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f018fbd9-0f16-4d2a-8ae5-1500713870ae · outbound

This paper cites Brendan McMahan, Brendan Avent, et al.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Brendan McMahan, Brendan Avent, et al

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:04.881061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ea0cc03b-c48e-41e7-b02f-1b91c17311bc · outbound

This paper cites A survey on concept drift adaptation.ACM Comput.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning A survey on concept drift adaptation.ACM Comput

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:04.793606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 88d52671-d594-4e88-99c7-736b8455894f · outbound

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

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Fedrc: Tackling diverse distribution shifts challenge in federated learning by robust clustering

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:04.660223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 220668c0-2d7b-4da4-a2d9-903b228b10fb · outbound

This paper cites An efficient framework for clustered federated learning.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning An efficient framework for clustered federated learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:04.545193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 866a31ea-45ca-4a79-9caa-3b99de06fe88 · outbound

This paper cites Accelerat- ing federated learning with cluster construction and hierarchical aggregation.IEEE Trans.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Accelerat- ing federated learning with cluster construction and hierarchical aggregation.IEEE Trans

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:04.376394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5d0e0dfb-16a7-4b7d-96d8-2a9bc11e192b · outbound

This paper cites Federated learning with hierarchical cluster- ing of local updates to improve training on non-iid data.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Federated learning with hierarchical cluster- ing of local updates to improve training on non-iid data

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:04.250216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bc2e484d-e20c-4f40-a7e6-ff718fcd4c55 · outbound

This paper cites Clustered federated learning: Model- agnostic distributed multitask optimization under privacy constraints.IEEE Trans.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Clustered federated learning: Model- agnostic distributed multitask optimization under privacy constraints.IEEE Trans

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:04.127854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0d7c72ff-26e2-445f-9d3e-5b799d261465 · outbound

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

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Fedsoft: Soft clustered federated learning with proximal local updating

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:03.923512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 27e60ac6-affb-4f2f-bdda-a649b3a5f65d · outbound

This paper cites Federated multi-task learning under a mixture of distributions.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Federated multi-task learning under a mixture of distributions

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:03.720412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 49001123-68c2-45b7-b8c5-1ec2e583c495 · outbound

This paper cites Multi-center federated learning: clients clustering for better personalization.World Wide Web (WWW), 26(1): 481–500, 2023.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Multi-center federated learning: clients clustering for better personalization.World Wide Web (WWW), 26(1): 481–500, 2023

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:03.515295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7f3302da-7607-4039-ad2b-a451e2dbcff1 · outbound

This paper cites Towards Federated Clustering: A Federated Fuzzy $c$-Means Algorithm (FFCM).

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Towards Federated Clustering: A Federated Fuzzy $c$-Means Algorithm (FFCM)

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:40:58.066278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 27a3b613-9856-4f81-9e5f-d75029462f2a · outbound

This paper cites Clustered federated learning in heterogeneous environment.IEEE Trans.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Clustered federated learning in heterogeneous environment.IEEE Trans

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:03.352127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6624d905-2241-4d82-9409-65415eb15871 · outbound

This paper cites an unresolved cited work.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:41:03.162769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6cfaa7af-fb70-48b7-b1df-d440b4f39305 · outbound

This paper cites A multi-model approach for handling concept drifting data in federated learning.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning A multi-model approach for handling concept drifting data in federated learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:02.983070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 385dd433-7120-400d-8cae-12bc94e4b5de · outbound

This paper cites Classifier clustering and feature alignment for federated learning under distributed concept drift.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Classifier clustering and feature alignment for federated learning under distributed concept drift

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:02.093370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 468eb90a-34ec-4559-9e26-5079b768a5f0 · outbound

This paper cites Drift detection and adaptation for federated learning in iot with adaptive device management.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Drift detection and adaptation for federated learning in iot with adaptive device management

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:01.713128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 359711eb-70e0-4d5d-9fb6-59f8fde0162a · outbound

This paper cites Casado, Dylan Lema, Marcos F.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Casado, Dylan Lema, Marcos F

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:01.458050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 873ebcc4-1c1c-42b2-b0c4-bb1b37c3d88c · outbound

This paper cites Asynchronous federated learning for sensor data with concept drift.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Asynchronous federated learning for sensor data with concept drift

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:01.214556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0840709b-38ae-42c9-b308-fa3e99b75a05 · outbound

This paper cites Federating from history in streaming federated learning.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Federating from history in streaming federated learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:00.938848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e69e9323-7412-432c-b302-208dd85e0b95 · outbound

This paper cites Adaptive federated learning in presence of concept drift.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Adaptive federated learning in presence of concept drift

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:00.745212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e46d46b0-ca2d-4a3b-8b69-d8b9539ad889 · outbound

This paper cites Flash: Concept drift adaptation in federated learning.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Flash: Concept drift adaptation in federated learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:00.560534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 04464e77-c0ef-40c6-a32d-f6b16a4581c4 · outbound

This paper cites Client-side adaptation to concept drift in federated learning.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Client-side adaptation to concept drift in federated learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:00.323305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f0a30c38-2a74-4716-8874-b3b0e7713d0f · outbound

This paper cites On the convergence of A class of adam-type algorithms for non-convex optimization.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning On the convergence of A class of adam-type algorithms for non-convex optimization

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:00.130997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fd6ff5c4-3783-4b71-ad3e-17c22fc64759 · outbound

This paper cites On the almost sure convergence of stochastic gradient descent in non-convex problems.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning On the almost sure convergence of stochastic gradient descent in non-convex problems

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:41:00.007105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 016f3204-fc3b-4fc3-9ea6-97a6b7769635 · outbound

This paper cites On the convergence of fedavg on non-iid data.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning On the convergence of fedavg on non-iid data

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:40:59.833842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f7355d06-49d1-4405-a8fa-75cf3f7ab49e · outbound

This paper cites Vincent Poor.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Vincent Poor

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:40:59.617427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cc1559fc-c131-4607-9899-94f7bb2d6652 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 28

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Source-reported events for the cited work

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Observation d24da47f-6afd-479b-863f-80af40fa8679 · outbound

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

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Learning multiple layers of features from tiny images

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T22:40:57.091690Z

Source-reported events for the cited work

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Observation 1023db3f-9551-4486-ae43-09c7c78e9aff · outbound

This paper cites Deep residual learning for im- age recognition.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Deep residual learning for im- age recognition

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:40:59.356130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fcd6174c-aa68-41cd-9a6d-9300dfef599b · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T22:40:57.306281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c898154-6762-4ad3-b382-20d93f4d830f · outbound

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

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Communication-efficient learning of deep networks from decentralized data

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:40:59.162478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8309103e-11c0-4a23-bb32-0bb15d7e7b0e · outbound

This paper cites Tuan Nguyen, Toan Tran, Yarin Gal, Philip H.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Tuan Nguyen, Toan Tran, Yarin Gal, Philip H

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:40:58.934360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 753d1d96-e8b8-4eb9-9bf2-515e5d5ef0f4 · outbound

This paper cites Tuan Nguyen, Philip H.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Tuan Nguyen, Philip H

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:40:58.622254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 015de732-2d91-4270-bfe1-b2e3ec0017f3 · outbound

This paper cites Information-theoretic analysis of unsupervised domain adapta- tion.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Information-theoretic analysis of unsupervised domain adapta- tion

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:40:58.344611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 77bb4636-0bf1-49ac-ae4b-9578e5168f45 · outbound

This paper cites Non-stationary domain generalization: Theory and algorithm.

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Non-stationary domain generalization: Theory and algorithm

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:40:58.245035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8a28bc92-5d58-4499-a1cb-3bfe42a9df61 · outbound

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

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T22:40:57.815989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.