Paper Citation Record · LEDGER

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data

As of 21 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2507.11471.

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

pith.paper-citation-record.v1
2507.11471 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:12:56.922505Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

38 of 38 outbound references displayed

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verified fuzzy27
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2da229f9-4d2b-4391-819d-ef2e6857bfc6 · outbound

This paper cites Iot connections worldwide 2022-2033.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Iot connections worldwide 2022-2033

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:54.479795Z

Source-reported events for the cited work

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Observation 93f5e14d-3470-4edb-8142-ee2bbdb86445 · outbound

This paper cites A yolo-based approach for fire and smoke detection in iot surveillance systems.International Journal of Advanced Computer Science & Applications, 15(1), 2024.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data A yolo-based approach for fire and smoke detection in iot surveillance systems.International Journal of Advanced Computer Science & Applications, 15(1), 2024

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:13:01.036589Z

Source-reported events for the cited work

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Observation b6788f71-11a6-4028-a28f-d32a22613dd2 · outbound

This paper cites Iot-enabled real-time traffic monitoring and control management for intelligent transportation systems.IEEE Internet of Things Journal, 2024.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Iot-enabled real-time traffic monitoring and control management for intelligent transportation systems.IEEE Internet of Things Journal, 2024

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:13:00.855234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 619f1594-aea2-46e6-ac8a-4af01944685b · outbound

This paper cites Real-time iot-powered ai system for monitoring and forecasting of air pollution in industrial environment.Ecotoxicology and Environmental Safety, 283:116856, 2024.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Real-time iot-powered ai system for monitoring and forecasting of air pollution in industrial environment.Ecotoxicology and Environmental Safety, 283:116856, 2024

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:13:00.615644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5a5c8540-7025-478f-ba58-83ca90b7a9be · outbound

This paper cites Internet of things (iot) based energy monitoring with esp 32 and using thingspeak.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Internet of things (iot) based energy monitoring with esp 32 and using thingspeak

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:13:00.492350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9edfcb55-c79b-45c9-b713-1934ac2f9d76 · outbound

This paper cites Electrical load forecasting using edge computing and federated learning.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Electrical load forecasting using edge computing and federated learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:13:00.373050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3ca87822-444f-4495-9350-c765ed90e8bd · outbound

This paper cites Machine learning model application and comparison in actuated traffic signal forecasting.Sensors, 23(15):6912, 2023.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Machine learning model application and comparison in actuated traffic signal forecasting.Sensors, 23(15):6912, 2023

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:13:00.242017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation de1cad7e-ec91-4b9b-b1bc-4b57e1b24221 · outbound

This paper cites A meta-graph deep learning framework for forecasting air pollutants in stockholm.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data A meta-graph deep learning framework for forecasting air pollutants in stockholm

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:13:00.125359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e551e511-0629-4e26-81de-207cdf30c3ba · outbound

This paper cites Techniques of time series modeling in complex systems.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Techniques of time series modeling in complex systems

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:13:00.035056Z

Source-reported events for the cited work

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Observation 3f0dc0e6-f35c-401a-ba9d-e7f53192c6b8 · outbound

This paper cites Federated Learning: Strategies for Improving Communication Efficiency.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Federated Learning: Strategies for Improving Communication Efficiency

Reference 10

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Observation 65303c88-98cc-45f9-9eb8-ee391434303a · outbound

This paper cites an unresolved cited work.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:12:59.896804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3389c2dc-9d3a-48c1-9703-64096d4604e4 · outbound

This paper cites an unresolved cited work.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:12:59.775382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2de4e05a-08e4-47bd-af5c-62c9a3af9198 · outbound

This paper cites Fast-convergent federated learning with adaptive weighting.IEEE Transactions on Cognitive Communications and Networking, 7(4):1078–1088, 2021.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Fast-convergent federated learning with adaptive weighting.IEEE Transactions on Cognitive Communications and Networking, 7(4):1078–1088, 2021

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:59.666856Z

Source-reported events for the cited work

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Observation 62e898e2-11df-455e-b9e9-89806d5ab6e9 · outbound

This paper cites Centralized and federated learning for predictive vnf autoscaling in multi-domain 5g networks and beyond.IEEE Transactions on Network and Service Management, 18(1):63–78, 2021.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Centralized and federated learning for predictive vnf autoscaling in multi-domain 5g networks and beyond.IEEE Transactions on Network and Service Management, 18(1):63–78, 2021

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:59.534511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f3d8b9f6-fcf9-4ffa-9dcf-03c0512c3ae4 · outbound

This paper cites an unresolved cited work.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:12:59.417490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 04db184b-35bc-432a-a7f6-00ff6549e321 · outbound

This paper cites Federated learning for 5g base station traffic forecasting.Computer Networks, 235:109950, 2023.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Federated learning for 5g base station traffic forecasting.Computer Networks, 235:109950, 2023

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:59.315026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7a40777c-8118-4649-b87a-d964b427132d · outbound

This paper cites Forecasting energy power consumption using federated learning in edge computing devices.Internet of Things, 25:101050, 2024.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Forecasting energy power consumption using federated learning in edge computing devices.Internet of Things, 25:101050, 2024

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:59.174635Z

Source-reported events for the cited work

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Observation 4f797f04-83bb-412f-9e45-52875a130dcc · outbound

This paper cites A solar forecasting framework based on feder- ated learning and distributed computing.Building and Environment, 225:109556, 2022.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data A solar forecasting framework based on feder- ated learning and distributed computing.Building and Environment, 225:109556, 2022

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:59.066345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 55fa213f-bc1d-4029-a688-6db501d88aef · outbound

This paper cites Data aging matters: Federated learning-based consumption prediction in smart homes via age-based model weighting.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Data aging matters: Federated learning-based consumption prediction in smart homes via age-based model weighting

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:58.929881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8dcd8d1a-9e4e-49a6-8f58-e75818f9525e · outbound

This paper cites Energy demand prediction with optimized clustering-based federated learning.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Energy demand prediction with optimized clustering-based federated learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:58.806974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c152e508-9509-45fb-8f17-36a111ca6021 · outbound

This paper cites Privacy enhanced energy prediction in smart building using federated learning.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Privacy enhanced energy prediction in smart building using federated learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:58.706400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c2c158a2-2493-4300-b700-73db29666108 · outbound

This paper cites Personalized federated learning for hetero- geneous residential load forecasting.Big Data Mining and Analytics, 6(4):421–432, 2023.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Personalized federated learning for hetero- geneous residential load forecasting.Big Data Mining and Analytics, 6(4):421–432, 2023

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:58.577521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 47eeb7b3-870e-495a-a0c8-7fae3ffe5d06 · outbound

This paper cites Hue: The hourly usage of energy dataset for buildings in british columbia.Data in brief, 23:103744, 2019.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Hue: The hourly usage of energy dataset for buildings in british columbia.Data in brief, 23:103744, 2019

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:58.450237Z

Source-reported events for the cited work

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Observation ed95830f-46ae-4c4d-a19c-19f2c92c6349 · outbound

This paper cites Federated learning-based multi-energy load forecasting method using cnn-attention-lstm model.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Federated learning-based multi-energy load forecasting method using cnn-attention-lstm model

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:58.356516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dca7b64b-e22e-48cc-a68f-d5a6219d4c28 · outbound

This paper cites Forecasting with exponential smoothing: the state space approach.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Forecasting with exponential smoothing: the state space approach

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:56.087649Z

Source-reported events for the cited work

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Observation 6830c508-3d66-4e8b-b32d-30adace90f69 · outbound

This paper cites John Wiley & Sons, 2015.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data John Wiley & Sons, 2015

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:56.141329Z

Source-reported events for the cited work

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Observation 5b24d537-1421-4353-be07-99c14757c3d9 · outbound

This paper cites Neural network forecasting for seasonal and trend time series.European journal of operational research, 160(2):501– 514, 2005.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Neural network forecasting for seasonal and trend time series.European journal of operational research, 160(2):501– 514, 2005

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:58.202387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 99022aa7-b0d1-4d0f-a41c-b6293b0bb2c6 · outbound

This paper cites Load forecasting via detrending and deseasoning.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Load forecasting via detrending and deseasoning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:58.061842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 988b94e6-cf2b-4d7a-ad48-f9dff2c56caf · outbound

This paper cites Forecasting and recombining time- series components by using neural networks.Journal of the Operational Research Society, 54(3):307–317, 2003.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Forecasting and recombining time- series components by using neural networks.Journal of the Operational Research Society, 54(3):307–317, 2003

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:57.957413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 98fbb18e-7ca9-426b-8802-2ab146a548d1 · outbound

This paper cites Modeling extreme climatic events using the generalized extreme value (gev) distribution.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Modeling extreme climatic events using the generalized extreme value (gev) distribution

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:57.831086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 09eafb7b-d741-4e04-92d6-12e4733565f3 · outbound

This paper cites Financial data analysis with two symmetric distri- butions.Astin Bulletin, 31(1):187–211, 2001.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Financial data analysis with two symmetric distri- butions.Astin Bulletin, 31(1):187–211, 2001

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:57.696690Z

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Observation 15413736-c59b-4dc3-9e07-8994e6e0e664 · outbound

This paper cites an unresolved cited work.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:12:57.595335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 475deecd-ef25-4eee-8698-5bd9d2fe0fba · outbound

This paper cites Informer: Beyond efficient transformer for long sequence time-series forecasting.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:56.608645Z

Source-reported events for the cited work

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Observation 1483d6f8-190e-4b4a-8a45-799aa351cf28 · outbound

This paper cites Sievers and T.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Sievers and T

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:57.458850Z

Source-reported events for the cited work

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Observation 59393670-3b4f-4c1e-b181-cf252864e6fb · outbound

This paper cites Towards a modular federated learning framework on edge devices.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Towards a modular federated learning framework on edge devices

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:57.318708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7676773e-9f73-469a-9c1a-82b2fb234ff8 · outbound

This paper cites https://www.ausgrid.com.au/ Industry/Our-Research/Data-to-share/Distribution-zone-substation-data,.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data https://www.ausgrid.com.au/ Industry/Our-Research/Data-to-share/Distribution-zone-substation-data,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:57.206514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d1a084f7-c4d9-48bf-9a65-fbb64e95ce8a · outbound

This paper cites Tune: A Research Platform for Distributed Model Selection and Training.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Tune: A Research Platform for Distributed Model Selection and Training

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:56.922505Z

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Observation 86fcfc2f-4a1e-4b4f-a409-977282c569ae · outbound

This paper cites an unresolved cited work.

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data Unresolved cited work

Reference 38

Resolution: parse uncertain; raw_fallback, observed 2026-08-06T17:13:01.133593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.