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Paper Citation Record · LEDGER

Tackling Data Heterogeneity in Federated Time Series Forecasting

As of 16 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 0 inbound Pith citation observations for arXiv:2411.15716.

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

pith.paper-citation-record.v1
2411.15716 v1

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:03:12.869688Z

measured 65 of 65 standing notices

One-hop event checks from named stored sources.

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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

65 of 65 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3239cfab-c886-46a2-88c6-25c688340055 · outbound

This paper cites Towards long-term time-series forecasting: Feature, pattern, and distribution,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Towards long-term time-series forecasting: Feature, pattern, and distribution,

Reference 1

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Observation 1f9d7784-8af6-4ca4-87b4-2d5c187caa13 · outbound

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

Tackling Data Heterogeneity in Federated Time Series Forecasting Informer: Beyond efficient transformer for long sequence time-series forecasting,

Reference 2

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Observation f9869df8-8da3-47d4-9b0a-7c03cd3719e2 · outbound

This paper cites itrans- former: Inverted transformers are effective for time series forecasting,.

Tackling Data Heterogeneity in Federated Time Series Forecasting itrans- former: Inverted transformers are effective for time series forecasting,

Reference 3

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Observation bc2ded25-4613-4566-a13a-d8670ed8e2ec · outbound

This paper cites Attention is all you need,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Attention is all you need,

Reference 4

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Observation fa22438f-991d-4b6c-b15c-3f98db24803c · outbound

This paper cites Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures.

Tackling Data Heterogeneity in Federated Time Series Forecasting Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures

Reference 5

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Observation 2cd4b456-a386-4d53-a160-b70f867fee3c · outbound

This paper cites Are transformers effective for time series forecasting?.

Tackling Data Heterogeneity in Federated Time Series Forecasting Are transformers effective for time series forecasting?

Reference 6

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Observation 882c2fb2-c32d-441d-81cc-096dcd498c01 · outbound

This paper cites Tsmixer: An all-mlp architecture for time series forecast-ing,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Tsmixer: An all-mlp architecture for time series forecast-ing,

Reference 7

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Observation ca8c865b-3e11-4086-a324-e1214d7ec948 · outbound

This paper cites Smart meter data privacy: A survey,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Smart meter data privacy: A survey,

Reference 8

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Source-reported events for the cited work

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Observation 5b1710c7-88cd-4544-9364-b1308313f177 · outbound

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

Tackling Data Heterogeneity in Federated Time Series Forecasting Communication-efficient learning of deep networks from decentralized data,

Reference 9

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Observation 5331bd08-58d6-41da-8023-b4c86fed05e5 · outbound

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

Tackling Data Heterogeneity in Federated Time Series Forecasting Federated learning on non-iid data silos: An experimental study,

Reference 10

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Observation e54d6305-e94f-495d-be3e-d4b97bf4bcac · outbound

This paper cites Heterogeneous feder- ated learning: State-of-the-art and research challenges,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Heterogeneous feder- ated learning: State-of-the-art and research challenges,

Reference 11

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Observation 276b7fa8-cb2e-49cd-bce2-8d8b92e38801 · outbound

This paper cites Dataset distillation by matching training trajectories,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Dataset distillation by matching training trajectories,

Reference 12

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Observation d032977d-0fdc-42c5-9a7a-beba52c278f6 · outbound

This paper cites Dataset distillation: A comprehensive re- view,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Dataset distillation: A comprehensive re- view,

Reference 13

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Observation 28ed0abd-06af-4531-a880-f185e76b9399 · outbound

This paper cites Graph Condensation: A Survey.

Tackling Data Heterogeneity in Federated Time Series Forecasting Graph Condensation: A Survey

Reference 14

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Observation e2cdaa76-2be6-4c78-afb0-f0d98fe68f4f · outbound

This paper cites Federated Learning via Synthetic Data.

Tackling Data Heterogeneity in Federated Time Series Forecasting Federated Learning via Synthetic Data

Reference 15

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Observation 35c80ab4-938e-41e9-8577-809fd566712c · outbound

This paper cites Feddm: Iterative distribution matching for communication-efficient federated learning,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Feddm: Iterative distribution matching for communication-efficient federated learning,

Reference 16

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Observation d893d604-9f97-44ab-a85f-285a88aa30e6 · outbound

This paper cites Meta knowledge condensation for fed- erated learning,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Meta knowledge condensation for fed- erated learning,

Reference 17

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Source-reported events for the cited work

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Observation 732ed832-d4e0-498d-88e3-a06c63b4a3cd · outbound

This paper cites An aggregation-free federated learning for tackling data heterogeneity,.

Tackling Data Heterogeneity in Federated Time Series Forecasting An aggregation-free federated learning for tackling data heterogeneity,

Reference 18

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Source-reported events for the cited work

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Observation 3acb4eb6-dbd9-4564-b8cc-deb5edf0cc49 · outbound

This paper cites Robust time series analysis and applications: An industrial perspective,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Robust time series analysis and applications: An industrial perspective,

Reference 19

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Source-reported events for the cited work

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Observation 25a21858-d2c5-49c0-8e87-849e012fbd0a · outbound

This paper cites Forecasting covid-19 dynamics: Clustering, generalized spatiotemporal attention, and impacts of mobility and geographic proximity,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Forecasting covid-19 dynamics: Clustering, generalized spatiotemporal attention, and impacts of mobility and geographic proximity,

Reference 20

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Source-reported events for the cited work

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Observation 22b9eb99-9db2-40d0-b125-422abecfbd13 · outbound

This paper cites Prompt federated learning for weather forecasting: toward foundation models on meteorological data,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Prompt federated learning for weather forecasting: toward foundation models on meteorological data,

Reference 21

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Source-reported events for the cited work

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Observation d8167227-3d8d-4deb-b526-18cefe3babdc · outbound

This paper cites Deep learning for time series forecasting: Tutorial and literature survey,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Deep learning for time series forecasting: Tutorial and literature survey,

Reference 22

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Source-reported events for the cited work

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Observation 0524b02d-ca42-4587-aa69-aeed29758e0c · outbound

This paper cites Distribution of residual autocorrelations in autoregressive-integrated moving average time series models,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Distribution of residual autocorrelations in autoregressive-integrated moving average time series models,

Reference 23

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Observation de37f21d-3533-466a-bc21-e94f9d4e32d4 · outbound

This paper cites Exponential smoothing: The state of the art,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Exponential smoothing: The state of the art,

Reference 24

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Observation 22957d0d-7597-4c6a-ad5b-805657e1be32 · outbound

This paper cites Forecasting, structural time series models and the kalman filter,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Forecasting, structural time series models and the kalman filter,

Reference 25

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Observation 2efe1cf5-ef15-4b3d-9c1f-87cc08f31035 · outbound

This paper cites Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network,

Reference 26

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Observation 564acecd-e660-46e1-ad0c-25f3bf826cda · outbound

This paper cites Introduction to convolutional neural networks,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Introduction to convolutional neural networks,

Reference 27

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Source-reported events for the cited work

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Observation 8d289ec6-1819-4e6f-b711-7940abf7cf4c · outbound

This paper cites Modeling long-and short-term temporal patterns with deep neural networks,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Modeling long-and short-term temporal patterns with deep neural networks,

Reference 28

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Observation f62d8f65-cc0c-4bd4-bb75-9779eae17fd9 · outbound

This paper cites Temporal pattern attention for multivariate time series forecasting,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Temporal pattern attention for multivariate time series forecasting,

Reference 29

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verified fuzzy
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Source-reported events for the cited work

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Observation d7a85ea4-d5e1-43d5-b980-cb4391d7b064 · outbound

This paper cites Transformers in time series: a survey,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Transformers in time series: a survey,

Reference 30

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Source-reported events for the cited work

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Observation 16f01740-4d69-49bc-b558-c7c8021bbf47 · outbound

This paper cites Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting,

Reference 31

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Observation a0bf4193-77d5-4b55-8252-c5f580c8a210 · outbound

This paper cites Argument discovery via crowdsourcing,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Argument discovery via crowdsourcing,

Reference 32

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 9ff9195d-b348-4998-be40-c861d45ec951 · outbound

This paper cites Federated unlearning for on-device recommendation,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Federated unlearning for on-device recommendation,

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 1141dd54-3449-48ac-8637-7aa87800d6b4 · outbound

This paper cites Hetefedrec: Federated recommender systems with model heterogeneity,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Hetefedrec: Federated recommender systems with model heterogeneity,

Reference 34

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Source-reported events for the cited work

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Observation a920a718-0b00-4a03-b9ae-a32f4ad8aa26 · outbound

This paper cites Hide your model: A parameter transmission-free federated recommender system,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Hide your model: A parameter transmission-free federated recommender system,

Reference 35

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 7c18dad7-21e1-4be4-90ff-e43c89544546 · outbound

This paper cites Manip- ulating visually aware federated recommender systems and its counter- measures,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Manip- ulating visually aware federated recommender systems and its counter- measures,

Reference 36

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raw_fallback, observed 2026-08-12T14:03:13.316188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 2ba18cc2-e78b-40b2-8fa2-fb8e3f15ad52 · outbound

This paper cites Federated machine learning: Concept and applications,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Federated machine learning: Concept and applications,

Reference 37

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source=pdf_text observed=2026-08-12T14:03:12.748366Z digest=sha256:1856b3c6484cff407b38e7c59cefa8d1f0a846291953cec46f75020a52e82ef2

Observation 080dd7cb-82ad-438c-84f7-5b3743ba8f39 · outbound

This paper cites Overcoming data sparsity in group recommendation,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Overcoming data sparsity in group recommendation,

Reference 38

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raw_fallback, observed 2026-08-12T14:03:13.293994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T14:03:12.753184Z digest=sha256:07373314ea06a780a0ea9dced1e2cadff97ec861e25ebc2ec8f69153d4d64a6f

Observation 6ca1206e-a23b-4ef7-867f-653aa9654d2b · outbound

This paper cites Exploiting shared representations for personalized federated learning,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Exploiting shared representations for personalized federated learning,

Reference 39

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source=pdf_text observed=2026-08-12T14:03:12.757289Z digest=sha256:ad06dd74722a641cc22a76636982cba5220e881ba6fde0e68410c069acc6a247

Observation 652045c3-f03f-43d3-8add-7d28c5cb19ba · outbound

This paper cites Ensemble distillation for robust model fusion in federated learning,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Ensemble distillation for robust model fusion in federated learning,

Reference 40

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source=pdf_text observed=2026-08-12T14:03:12.762055Z digest=sha256:02564e87c6cba8e229ab85cbbf6ac1e940062e9ca58d8e0494a946867ca0441c

Observation 821b20c9-14c4-4504-85b1-c98475d30324 · outbound

This paper cites Fedbe: Making bayesian model ensemble applicable to federated learning,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Fedbe: Making bayesian model ensemble applicable to federated learning,

Reference 41

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verified fuzzy
raw_fallback, observed 2026-08-12T14:03:13.266087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T14:03:12.766152Z digest=sha256:2432d5a09eec371dae989dca3f68c067b5a86625ad9b4e68f90680607ca8a4a1

Observation cfdb3ed7-36c8-47c5-a870-24624c714880 · outbound

This paper cites Feddg: Federated do- main generalization on medical image segmentation via episodic learn- ing in continuous frequency space,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Feddg: Federated do- main generalization on medical image segmentation via episodic learn- ing in continuous frequency space,

Reference 42

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raw_fallback, observed 2026-08-12T14:03:13.252623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T14:03:12.770173Z digest=sha256:e56872a048fa4407cc5d736883bcefa390da4b9eba9d3c47260167eea935fc15

Observation a2b54d10-246f-4303-8fe4-27e56cb92160 · outbound

This paper cites Fedmix: Approximation of mixup under mean augmented federated learning,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Fedmix: Approximation of mixup under mean augmented federated learning,

Reference 43

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verified fuzzy
raw_fallback, observed 2026-08-12T14:03:13.237278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T14:03:12.774435Z digest=sha256:870d59485fe4a9dfb9bcfa799ddad76bb1d85831e17c5b0b916326545e091ac1

Observation fbfdc417-17fa-4d59-9f0f-99c3e39f4fa2 · outbound

This paper cites Federated optimization in heterogeneous networks,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Federated optimization in heterogeneous networks,

Reference 44

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T14:03:12.778641Z digest=sha256:036c34c5c3c1437be13fbf125339ae3e7b3c60165f783fab462547d79e8af378

Observation 37e5c39d-ec65-4136-a7e9-a3700ce01e35 · outbound

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

Tackling Data Heterogeneity in Federated Time Series Forecasting Scaffold: Stochastic controlled averaging for federated learn- ing,

Reference 45

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source=pdf_text observed=2026-08-12T14:03:12.782300Z digest=sha256:a33e26c9d9b6b050fe942f0ae9fa2c8a24d72d694d56fc61e996cb2c1746bc1b

Observation e48c1e3a-0503-4612-b01f-8445bfca4f5d · outbound

This paper cites Federated Learning Based on Dynamic Regularization.

Tackling Data Heterogeneity in Federated Time Series Forecasting Federated Learning Based on Dynamic Regularization

Reference 46

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source=pdf_text observed=2026-08-12T14:03:12.786378Z digest=sha256:9fec7826192671581b0441cd04373277fee1ef021fe1a1ec8a1bed5d53560371

Observation e959c3a7-d25f-472f-819a-5b1fd86ffbbf · outbound

This paper cites Elastic aggregation for federated optimization,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Elastic aggregation for federated optimization,

Reference 47

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verified fuzzy
raw_fallback, observed 2026-08-12T14:03:13.202748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T14:03:12.791680Z digest=sha256:67015044889d6d1c62b11e802751f1ed04e491369ee12c892b0afc67e8e883c7

Observation a3ecde36-1bd7-45f3-927e-400c8792d772 · outbound

This paper cites Fair federated learning under domain skew with local consistency and domain diversity,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Fair federated learning under domain skew with local consistency and domain diversity,

Reference 48

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verified fuzzy
raw_fallback, observed 2026-08-12T14:03:13.189373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T14:03:12.796833Z digest=sha256:fc04f658da5a7d2b22240149def867f34119011032e19a0c51275770d33a155d

Observation bf4f6bd0-acc7-4629-9098-317d5423a8b6 · outbound

This paper cites Distilled One-Shot Federated Learning.

Tackling Data Heterogeneity in Federated Time Series Forecasting Distilled One-Shot Federated Learning

Reference 49

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source=pdf_text observed=2026-08-12T14:03:12.802863Z digest=sha256:a1d35889c266d62607f9d48cdf59392dd60f08aa52f95a124e5465db0d8ef375

Observation 586a55c5-10b3-4880-a97e-6ee4ce2bf472 · outbound

This paper cites Fedsynth: Gradient compression via synthetic data in federated learning,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Fedsynth: Gradient compression via synthetic data in federated learning,

Reference 50

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verified fuzzy
raw_fallback, observed 2026-08-12T14:03:13.176273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T14:03:12.807340Z digest=sha256:ac1638865464d8669342f06a4a4d8ba5719f9cca5e111d9a27dde58e7924153f

Observation 88cb9065-a905-4c6a-8b13-17081056ed99 · outbound

This paper cites Dense: Data-free one-shot federated learning,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Dense: Data-free one-shot federated learning,

Reference 51

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source=pdf_text observed=2026-08-12T14:03:12.811469Z digest=sha256:7e15ea99c6c323bbec27312db8ba93b0b8e075444b9f4e9e9d093e91e09bfb5b

Observation 876745ed-891d-4673-8222-673447c8d64a · outbound

This paper cites Keyword-aware continuous knn query on road networks,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Keyword-aware continuous knn query on road networks,

Reference 52

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verified fuzzy
raw_fallback, observed 2026-08-12T14:03:13.155611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T14:03:12.815540Z digest=sha256:8d6caefe37f8c99fde77c0acdb68dcd028637d6e6de3a0ad7367258530b04f61

Observation 2ddb2cd2-872b-446c-81ed-b688ae077f69 · outbound

This paper cites Enhancing one- shot federated learning through data and ensemble co-boosting,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Enhancing one- shot federated learning through data and ensemble co-boosting,

Reference 53

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verified fuzzy
raw_fallback, observed 2026-08-12T14:03:13.143130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T14:03:12.819298Z digest=sha256:1a9cbc603a4da872352b6517dc5538563680cda076d6524d9dc4ecbbb14759f2

Observation 5ad38445-ca06-48b8-8d5c-228d2fb837f3 · outbound

This paper cites Dynafed: Tackling client data heterogeneity with global dynamics,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Dynafed: Tackling client data heterogeneity with global dynamics,

Reference 54

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raw_fallback, observed 2026-08-12T14:03:13.127934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T14:03:12.823729Z digest=sha256:1ec572b3e417276e885fc48f93264f584ffbdefee04f1c781650bf4f80e52185

Observation f1cabc09-762a-4037-b2e5-4ef191dd3c6f · outbound

This paper cites Computing crowd consensus with partial agreement,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Computing crowd consensus with partial agreement,

Reference 55

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T14:03:12.827548Z digest=sha256:418356e1c2e6b3c533999d619e72c00a28d9267e884fc6f4ac53e6ee33d23bbc

Observation 936bb24f-e36b-4aa1-a3ad-cd6ce440f44b · outbound

This paper cites Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting.

Tackling Data Heterogeneity in Federated Time Series Forecasting Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting

Reference 56

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source=pdf_text observed=2026-08-12T14:03:12.831645Z digest=sha256:40fbb39241ee1e37776fbabd02153c8b2d80f3b6c6037866ad3c0ce90492b72b

Observation 27202d80-0d3b-4188-9012-22754d42a365 · outbound

This paper cites A federated large language model for long-term time series forecasting.

Tackling Data Heterogeneity in Federated Time Series Forecasting A federated large language model for long-term time series forecasting

Reference 57

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source=pdf_text observed=2026-08-12T14:03:12.836529Z digest=sha256:35795ee010fe9f2ac68cdfa687ff60d9e7bb577a47c254de08d00bd1f8202fb3

Observation 397e63ce-259a-4b45-bbbf-dc81aa5faac6 · outbound

This paper cites Multi- participant vertical federated learning based time series prediction,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Multi- participant vertical federated learning based time series prediction,

Reference 58

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verified fuzzy
raw_fallback, observed 2026-08-12T14:03:13.107145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T14:03:12.840776Z digest=sha256:3e62bea2643ca36b4aaf4db372bfb6a43d6ebda3237fc150b279bf5b53747dc6

Observation a7fccbd6-1e3b-4f18-9352-b97be45dd0b0 · outbound

This paper cites Dataset Condensation with Gradient Matching.

Tackling Data Heterogeneity in Federated Time Series Forecasting Dataset Condensation with Gradient Matching

Reference 59

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source=pdf_text observed=2026-08-12T14:03:12.845108Z digest=sha256:e67fa569ae1c4089fed5c868b3db49202b2be0af055a6c7543d0fb69791b38d3

Observation 9c33ad32-05f8-484c-9569-4fd7e1c181fe · outbound

This paper cites Dataset distillation using parameter pruning,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Dataset distillation using parameter pruning,

Reference 60

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verified fuzzy
raw_fallback, observed 2026-08-12T14:03:13.094145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T14:03:12.849936Z digest=sha256:fe3ef5012dd6431309896f45105cfea164bb1c9024704fafab69864ceca85411

Observation 037779ec-4c1f-4cc3-ade6-d40e46d95038 · outbound

This paper cites Minimizing the accu- mulated trajectory error to improve dataset distillation,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Minimizing the accu- mulated trajectory error to improve dataset distillation,

Reference 61

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verified fuzzy
raw_fallback, observed 2026-08-12T14:03:13.080128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T14:03:12.853830Z digest=sha256:ab185846d3671c4b9bb7ba074b8a7530724538ed1c7125b6c7c140a4ca33f79e

Observation 465104ae-2c54-419a-b078-a6f85eba4559 · outbound

This paper cites A comprehensive survey of privacy- preserving federated learning: A taxonomy, review, and future direc- tions,.

Tackling Data Heterogeneity in Federated Time Series Forecasting A comprehensive survey of privacy- preserving federated learning: A taxonomy, review, and future direc- tions,

Reference 62

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:03:12.857803Z digest=sha256:bbf55479bc2036e22001e2f660769a8a6fa00014fc4d95dac9a6a82e4ac09900

Observation f9283fb1-0a02-4490-a543-3affdc19bc17 · outbound

This paper cites On the importance of initialization and momentum in deep learning,.

Tackling Data Heterogeneity in Federated Time Series Forecasting On the importance of initialization and momentum in deep learning,

Reference 63

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no resolver link, observed 2026-08-12T14:03:12.862017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:03:12.862017Z digest=sha256:6b313b9236df4e715061ffbe84ba70e89f0119436ea41c6cb527f8433e293857

Observation a7cc6957-efd5-4020-9576-0de0264f0073 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Tackling Data Heterogeneity in Federated Time Series Forecasting Adam: A Method for Stochastic Optimization

Reference 64

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no resolver link, observed 2026-08-12T14:03:12.865796Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T14:03:12.865796Z digest=sha256:2f4a767e9de4cbc6a961625a882ecc640e512608f3a8a751f538c3e584eb1979

Observation fa9db371-6242-4e92-b0f1-456711d907e5 · outbound

This paper cites Comprehensive privacy analysis on federated recommender system against attribute inference attacks,.

Tackling Data Heterogeneity in Federated Time Series Forecasting Comprehensive privacy analysis on federated recommender system against attribute inference attacks,

Reference 65

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source=pdf_text observed=2026-08-12T14:03:12.869688Z digest=sha256:a72aee141e3a3c7b3f1a252b4dde42aba30e44e28484b368b256b7c647cd7b3c

Pith citing papers

No inbound Pith citation observations are available.