Paper Citation Record · LEDGER

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction

As of 19 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2504.17528.

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

pith.paper-citation-record.v1
2504.17528 v1

Coverage vector

measured 42 of 42 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-16T10:44:37.005945Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

42 of 42 outbound references displayed

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verified fuzzy17
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External citation measurements

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Outbound references

Observation 562ae191-0cdc-46a6-8b3f-5b89b4346c4f · outbound

This paper cites Edge intelligence: Paving the last mile of artificial intelligence with edge computing,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Edge intelligence: Paving the last mile of artificial intelligence with edge computing,

Reference 1

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Observation 03bcc2f7-39e5-4ebc-b693-11ea026bb326 · outbound

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

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Communication-efficient learning of deep networks from decentralized data,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:44:37.554692Z

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Observation 179347fa-16cb-4060-9fc6-b8f89f60bdcc · outbound

This paper cites Accelerating federated learning with data and model parallelism in edge computing,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Accelerating federated learning with data and model parallelism in edge computing,

Reference 3

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Observation 2dbb5237-77b8-409c-814c-da7bb9bb0eeb · outbound

This paper cites Federated learning over wireless networks: Convergence analysis and resource allocation,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Federated learning over wireless networks: Convergence analysis and resource allocation,

Reference 4

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Observation befd62a2-1124-4808-a248-70bfc985c524 · outbound

This paper cites Accelerating and securing federated learning with stateless in-network aggregation at the edge,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Accelerating and securing federated learning with stateless in-network aggregation at the edge,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:44:37.521505Z

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Observation 151b3220-cff3-4bc1-9214-8de15043fd2d · outbound

This paper cites Federated optimization in heterogeneous networks,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Federated optimization in heterogeneous networks,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T10:44:36.837871Z

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Observation 05aea188-3076-4d0c-ab73-39b2900eef5e · outbound

This paper cites The limitations of federated learning in sybil settings,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction The limitations of federated learning in sybil settings,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T10:44:36.843178Z

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Observation d97fbf88-30e9-4fe1-a815-82ee5f03fce9 · outbound

This paper cites Scaffold: Stochastic controlled averaging for federated learn- ing,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Scaffold: Stochastic controlled averaging for federated learn- ing,

Reference 8

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Observation 6d2fe4af-a5e8-43a8-8690-237907e866b3 · outbound

This paper cites Stem: A stochastic two-sided momentum algorithm achieving near-optimal sample and communication complexities for federated learning,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Stem: A stochastic two-sided momentum algorithm achieving near-optimal sample and communication complexities for federated learning,

Reference 9

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Observation 75f2608f-583f-4cdb-9b18-04b2f112558a · outbound

This paper cites Communication-efficient federated learn- ing with accelerated client gradient,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Communication-efficient federated learn- ing with accelerated client gradient,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:44:37.459503Z

Source-reported events for the cited work

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Observation cead38d8-260b-4b1e-b8f5-5b596229efae · outbound

This paper cites Federated learning on non-iid data silos: An experimental study,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Federated learning on non-iid data silos: An experimental study,

Reference 11

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Observation a9a2731d-0728-4485-aca6-454c669b4406 · outbound

This paper cites Tackling the objective inconsistency problem in heterogeneous federated optimiza- tion,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Tackling the objective inconsistency problem in heterogeneous federated optimiza- tion,

Reference 12

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Observation 4e60a937-d410-4997-afcb-3428b5fac64f · outbound

This paper cites To talk or to work: Dynamic batch sizes assisted time efficient federated learn- ing over future mobile edge devices,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction To talk or to work: Dynamic batch sizes assisted time efficient federated learn- ing over future mobile edge devices,

Reference 13

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Observation ba1db47c-377d-4fbd-9deb-1bba3c53e34b · outbound

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

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Reading digits in natural images with unsupervised feature learning,

Reference 14

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Observation 69cc9cf8-9aee-401c-bb24-9052d1f2f888 · outbound

This paper cites Free-rider attacks on model aggregation in federated learning,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Free-rider attacks on model aggregation in federated learning,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:44:37.400437Z

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Observation 37596750-69a7-460d-b96c-1f37c42a36ed · outbound

This paper cites Enabling long-term cooperation in cross-silo federated learning: A repeated game perspective,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Enabling long-term cooperation in cross-silo federated learning: A repeated game perspective,

Reference 16

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Observation 7e6281c4-d24f-4458-8962-e2cfeabb83d7 · outbound

This paper cites Free-riders in Federated Learning: Attacks and Defenses.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Free-riders in Federated Learning: Attacks and Defenses

Reference 17

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Observation e2d3a061-9179-464a-b0b2-35946128d1d8 · outbound

This paper cites Toward free-riding attack on cross-silo federated learning through evolutionary game,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Toward free-riding attack on cross-silo federated learning through evolutionary game,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:44:37.370009Z

Source-reported events for the cited work

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Observation 66576304-dbd7-4ac5-95d2-1346eea524c5 · outbound

This paper cites FedCM: Federated Learning with Client-level Momentum.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction FedCM: Federated Learning with Client-level Momentum

Reference 19

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Observation 80420934-1983-491e-a7c3-d6bdfbdf5a43 · outbound

This paper cites Optimization methods for large- scale machine learning,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Optimization methods for large- scale machine learning,

Reference 20

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Observation 38b5ba8f-a5be-4d18-a331-e9621460fa1b · outbound

This paper cites Adaptive federated learning in resource constrained edge computing systems,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Adaptive federated learning in resource constrained edge computing systems,

Reference 21

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Observation 519435eb-3837-4338-9182-aa173b3bc155 · outbound

This paper cites Feder- ated learning under heterogeneous and correlated client availability,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Feder- ated learning under heterogeneous and correlated client availability,

Reference 22

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Observation b9b610cd-9bb9-4914-b0f4-f04ed7c3abb7 · outbound

This paper cites Federated learning with flexible control,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Federated learning with flexible control,

Reference 23

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Observation 62b6c532-2c82-4724-a264-16ecf3c23005 · outbound

This paper cites Dynamite: Dynamic interplay of mini-batch size and aggregation frequency for federated learning with static and streaming dataset,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Dynamite: Dynamic interplay of mini-batch size and aggregation frequency for federated learning with static and streaming dataset,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:44:37.309398Z

Source-reported events for the cited work

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Observation cdcee5c8-b92f-45b1-bcef-8813e32b1885 · outbound

This paper cites Advances and open problems in federated learning,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Advances and open problems in federated learning,

Reference 25

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Observation b7c989b5-cfa1-4e29-b181-e9ea2e3822d1 · outbound

This paper cites Fedmos: Taming client drift in federated learning with double momentum and adaptive selection,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Fedmos: Taming client drift in federated learning with double momentum and adaptive selection,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:44:37.284683Z

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Observation 5e60c3cd-4fba-43f9-a7e2-0eb454329000 · outbound

This paper cites On the Convergence of FedAvg on Non-IID Data.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction On the Convergence of FedAvg on Non-IID Data

Reference 27

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This paper cites Becker and R.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Becker and R

Reference 28

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Observation adb55a71-80b8-4309-ba3f-d27689a906d3 · outbound

This paper cites Deep residual learning for image recognition,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Deep residual learning for image recognition,

Reference 29

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Observation c288386a-383b-48ea-88f7-79fead8ae4da · outbound

This paper cites Autofl: A bayesian game approach for autonomous client participation in federated edge learning,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Autofl: A bayesian game approach for autonomous client participation in federated edge learning,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:44:37.260038Z

Source-reported events for the cited work

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Observation 91bd3d68-75c6-4621-b67b-47ccc50dcbee · outbound

This paper cites LEAF: A Benchmark for Federated Settings.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction LEAF: A Benchmark for Federated Settings

Reference 31

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Observation cb358c5c-041a-4e4d-91f7-42a31981cd5d · outbound

This paper cites A hierarchical knowledge transfer framework for heterogeneous federated learning,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction A hierarchical knowledge transfer framework for heterogeneous federated learning,

Reference 32

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Observation 79d49283-0cde-436c-b3f1-58d26e4792c3 · outbound

This paper cites Distributionally robust federated learning for network traffic classification with noisy labels,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Distributionally robust federated learning for network traffic classification with noisy labels,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:44:37.235505Z

Source-reported events for the cited work

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Observation 6bc4f8ae-1723-4984-99ea-c4af1f72a742 · outbound

This paper cites Heterogeneity- aware federated learning with adaptive client selection and gradient compression,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Heterogeneity- aware federated learning with adaptive client selection and gradient compression,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:44:37.220293Z

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Observation be3ce388-467e-4dac-a78e-6c8b821db0c6 · outbound

This paper cites Internal cross-layer gradients for extending homogeneity to heterogeneity in federated learning,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Internal cross-layer gradients for extending homogeneity to heterogeneity in federated learning,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:44:37.204351Z

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Observation 40757bab-9ab8-405d-bbf3-7fa4d4f208ff · outbound

This paper cites Can federated learning clients be lightweight? a plug-and-play symmetric conversion module,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Can federated learning clients be lightweight? a plug-and-play symmetric conversion module,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:44:37.188392Z

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Observation b205056e-e0e6-48a2-832b-a6f6d1a480f5 · outbound

This paper cites Federated Learning Based on Dynamic Regularization.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Federated Learning Based on Dynamic Regularization

Reference 37

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Observation 7cb39173-c24e-400c-b25f-ba940013d9cb · outbound

This paper cites Feddc: Federated learning with non-iid data via local drift decoupling and correction,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Feddc: Federated learning with non-iid data via local drift decoupling and correction,

Reference 38

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This paper cites Towards flexible device participation in federated learning,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Towards flexible device participation in federated learning,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:44:37.163663Z

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Observation 18b85d2e-b026-49c4-b0d9-86e5c8d14a2f · outbound

This paper cites Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 40

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Observation 3e4b7f57-d295-4e35-8c94-2f96762ad109 · outbound

This paper cites Ma- chine learning with adversaries: Byzantine tolerant gradient descent,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Ma- chine learning with adversaries: Byzantine tolerant gradient descent,

Reference 41

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Observation 28c9835a-3182-4499-8bd3-384f5017bd9c · outbound

This paper cites Feder- ated learning with compression: Unified analysis and sharp guarantees,.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Feder- ated learning with compression: Unified analysis and sharp guarantees,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-16T10:44:37.005945Z

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Pith citing papers

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