Paper Citation Record · LEDGER

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks

As of 7 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2507.02021.

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

pith.paper-citation-record.v1
2507.02021 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:49:34.918562Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

23 of 23 outbound references displayed

verified exact0
verified fuzzy22
unresolved1
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a54f9111-dad6-4eab-9f7d-36fc9b2ec231 · outbound

This paper cites On softwarization of intelligence in 6G networks for ultra-fast optimal policy selection: Challenges and opportunities,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks On softwarization of intelligence in 6G networks for ultra-fast optimal policy selection: Challenges and opportunities,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:38.949165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5aa3e935-3508-41d0-83d2-1aa23fc9be00 · outbound

This paper cites Privacy-preserving data-driven learning mod- els for emerging communication networks: A comprehensive sur- vey,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks Privacy-preserving data-driven learning mod- els for emerging communication networks: A comprehensive sur- vey,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T20:49:32.909944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8cdf758-8b39-4808-94f9-ecccfe554e32 · outbound

This paper cites A survey on federated learning,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks A survey on federated learning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:38.807652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3a0f2da2-72e2-4af0-9a1d-46892efc16d2 · outbound

This paper cites HCP: heterogeneous computing platform for federated learning based collaborative content caching towards 6g networks,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks HCP: heterogeneous computing platform for federated learning based collaborative content caching towards 6g networks,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:38.633338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7e37ed2e-db00-4348-a59a-1b7af5f25c18 · outbound

This paper cites Privacy-preserving federated- learning-based net-energy forecasting,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks Privacy-preserving federated- learning-based net-energy forecasting,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:38.477327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 50adc746-30d6-4d64-b74d-44c42664fca9 · outbound

This paper cites Privacy-preserving and efficient decentralized federated learning-based energy theft detector,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks Privacy-preserving and efficient decentralized federated learning-based energy theft detector,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:38.280003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 60bf914a-b2c8-4bb3-82d9-91d17d3bb796 · outbound

This paper cites On COVID-19 prediction using asynchronous federated learning-based agile radiograph screening booths,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks On COVID-19 prediction using asynchronous federated learning-based agile radiograph screening booths,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:38.100140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 84382b43-8665-4793-a50b-f13a7d010418 · outbound

This paper cites Asynchronous federated learning-based ECG analysis for arrhythmia detection,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks Asynchronous federated learning-based ECG analysis for arrhythmia detection,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:37.902499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d082e4e7-be88-42bc-8ec2-f8746d11b051 · outbound

This paper cites Toward asynchronously weight updating federated learning for AI-on-edge IoT systems,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks Toward asynchronously weight updating federated learning for AI-on-edge IoT systems,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:37.690165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 66c33ed3-a2c9-4d8b-aa57-5eb0a3742346 · outbound

This paper cites A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:37.535405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6bf20ccf-e3ba-48c5-9203-c70b6928c50f · outbound

This paper cites Adaboost-based security level classification of mobile intelligent terminals,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks Adaboost-based security level classification of mobile intelligent terminals,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:37.337229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 476e3df9-bf26-4c76-8d99-8d07216c0757 · outbound

This paper cites Joint provisioning of QoS and se- curity in IoD networks: Classical optimization meets AI,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks Joint provisioning of QoS and se- curity in IoD networks: Classical optimization meets AI,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:37.158090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0baeb346-48cd-46a6-aec8-451846776800 · outbound

This paper cites CICIoT2023: A real-time dataset and benchmark for large- scale attacks in IoT environment,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks CICIoT2023: A real-time dataset and benchmark for large- scale attacks in IoT environment,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:37.015226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b763b505-9664-46a9-a33f-66440df3e18f · outbound

This paper cites Data resampling for federated learning with non-IID labels,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks Data resampling for federated learning with non-IID labels,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:36.821475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9f7daa3e-d8a9-48b0-9f54-d48bbe906065 · outbound

This paper cites AdaBoost-CNN: an adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks AdaBoost-CNN: an adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:36.637285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f2603f46-7343-40b3-9751-ead334709d1e · outbound

This paper cites LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computa- tional complexity on IID and non-IID intensive care data,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computa- tional complexity on IID and non-IID intensive care data,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:36.440808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 655d47dd-4912-45d9-86a7-6a480555dfbd · outbound

This paper cites Differential privacy for deep and federated learning: A survey,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks Differential privacy for deep and federated learning: A survey,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:36.269694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d148bc92-673b-4155-b4fc-94a27332a3f3 · outbound

This paper cites Federated learning with differential privacy: Algorithms and performance analysis,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks Federated learning with differential privacy: Algorithms and performance analysis,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:36.079212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0075fad0-8567-4f16-9ec3-588a8970ed20 · outbound

This paper cites Evaluating differentially private machine learning in practice,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks Evaluating differentially private machine learning in practice,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:35.927064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 175f1fa6-ca95-433f-a0bc-63f54fe6ab7c · outbound

This paper cites Communication and computation efficiency in federated learning: A survey,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks Communication and computation efficiency in federated learning: A survey,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:35.774349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f50b097f-5faa-4ec2-9cc6-334a7e34035b · outbound

This paper cites Communication-efficient federated learning via quantized compressed sensing,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks Communication-efficient federated learning via quantized compressed sensing,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:35.564305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 128c38ac-cd35-4f02-a7b8-ca4d1df07f83 · outbound

This paper cites A robust federated learning approach for combating attacks against IoT systems under non-IID challenges,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks A robust federated learning approach for combating attacks against IoT systems under non-IID challenges,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:35.372153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 066b3a43-7ae5-42cc-8baf-43ab7b90c61b · outbound

This paper cites Combating IoT attacks in AI-driven networks via robust and resource-efficient federated learning,.

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks Combating IoT attacks in AI-driven networks via robust and resource-efficient federated learning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:35.165884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.