Paper Citation Record · LEDGER

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks

As of 14 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2502.05041.

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

pith.paper-citation-record.v1
2502.05041 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T20:30:46.448773Z

measured 36 of 36 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

36 of 36 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 569191d9-c46c-4087-8821-301613016a13 · outbound

This paper cites Global Status Report for Buildings and Construction,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Global Status Report for Buildings and Construction,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.978943Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9ecab53e-5692-498d-95c7-f63edae6e846 · outbound

This paper cites Net zero coalition,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Net zero coalition,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.949913Z

Source-reported events for the cited work

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Observation a38ef8c0-2462-4f7e-975b-2a398324d82e · outbound

This paper cites Anomaly detection: A survey,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Anomaly detection: A survey,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.935505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ed11794e-76fe-4489-9016-ca8f9a08cc70 · outbound

This paper cites High-dimensional energy consumption anomaly detection: A deep learning-based method for detecting anoma- lies,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks High-dimensional energy consumption anomaly detection: A deep learning-based method for detecting anoma- lies,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.922263Z

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Observation c732e5ef-3856-4d99-9cc3-04c3cdfe7775 · outbound

This paper cites Enhanced anomaly-based fault detection system in electrical power grids,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Enhanced anomaly-based fault detection system in electrical power grids,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.908310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6b326e93-b1fe-4dfa-91e4-8e6866334f96 · outbound

This paper cites An anomaly detection framework for identifying energy theft and defective meters in smart grids,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks An anomaly detection framework for identifying energy theft and defective meters in smart grids,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.894732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 64b3ba5a-ceba-4ca0-b7ea-be5258cda9f7 · outbound

This paper cites Distributed anomaly detection in smart grids: a federated learning-based approach,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Distributed anomaly detection in smart grids: a federated learning-based approach,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.881126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 35503945-2992-42a6-9e9a-84cfa12f5d5f · outbound

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

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Advances and open problems in federated learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.867454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b860e179-6449-4882-b5ba-f68dd92eb86d · outbound

This paper cites Privacy preservation in federated learning: An insightful survey from the gdpr perspective,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Privacy preservation in federated learning: An insightful survey from the gdpr perspective,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.854522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f3f62aae-6ba1-4050-bd84-7b0ff6f400e6 · outbound

This paper cites Distributed load forecasting using smart meter data: Federated learning with recurrent neural networks,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Distributed load forecasting using smart meter data: Federated learning with recurrent neural networks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.841273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 56cba25f-7eb4-4af0-8d9e-fb54935f1be5 · outbound

This paper cites Asynchronous adaptive federated learning for distributed load forecasting with smart meter data,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Asynchronous adaptive federated learning for distributed load forecasting with smart meter data,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.828154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8fadace8-ab2d-4364-b435-885873f756e4 · outbound

This paper cites When the curious abandon honesty: Federated learning is not private,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks When the curious abandon honesty: Federated learning is not private,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.814379Z

Source-reported events for the cited work

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Observation cd5b3fd7-6f09-4a33-b7a5-de293c771d5f · outbound

This paper cites Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models

Reference 13

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Source-reported events for the cited work

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Observation a3920ba7-bb62-408a-8679-bbda73e5343e · outbound

This paper cites Vulnerabilities in federated learning,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Vulnerabilities in federated learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.799879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 42de6770-6e10-49ca-a1e4-1650d0163cc1 · outbound

This paper cites Delving into the adversarial robustness of federated learning,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Delving into the adversarial robustness of federated learning,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.785778Z

Source-reported events for the cited work

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Observation c35a63db-3844-4845-bd70-6fc1de7c43f5 · outbound

This paper cites Gear: a margin-based federated adver- sarial training approach,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Gear: a margin-based federated adver- sarial training approach,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.771573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3b3dd983-7d92-458e-9ad8-98f558b5a3d1 · outbound

This paper cites Explaining and harnessing adversarial examples,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Explaining and harnessing adversarial examples,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.757409Z

Source-reported events for the cited work

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Observation d94444df-eaa2-4b3c-b9bb-b802c55af3da · outbound

This paper cites Towards deep learning models resistant to adversarial attacks,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Towards deep learning models resistant to adversarial attacks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.743128Z

Source-reported events for the cited work

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Observation b07ed30b-bab0-456a-abe3-89a072b71afa · outbound

This paper cites Adversarial attacks on deep neural networks for time series classification,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Adversarial attacks on deep neural networks for time series classification,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.728685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8ebe343d-4a29-4da9-aa23-d5bb50e2e891 · outbound

This paper cites Adversarial examples in deep learning for multivariate time series regression,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Adversarial examples in deep learning for multivariate time series regression,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.714571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2b94111d-2196-408b-8fda-d9c17b9fe907 · outbound

This paper cites LSTM based long-term energy consumption prediction with periodicity,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks LSTM based long-term energy consumption prediction with periodicity,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.700952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 05927539-8d57-4ba8-9e08-178362f16b89 · outbound

This paper cites Transformer-based model for electrical load forecasting,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Transformer-based model for electrical load forecasting,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.687052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7777d597-4b37-4d98-b4f8-1f4dfc83b7c5 · outbound

This paper cites Power consumption predicting and anomaly detection based on transformer and k-means,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Power consumption predicting and anomaly detection based on transformer and k-means,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.673415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 281177db-afab-45bb-abeb-926ea0d82cf8 · outbound

This paper cites Privacy-preserving federated learning against label-flipping attacks on non-iid data,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Privacy-preserving federated learning against label-flipping attacks on non-iid data,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.658694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2a704563-e3a7-4ea1-a394-5f14bd7262dd · outbound

This paper cites A novel approach for detecting anomalous energy consumption based on micro-moments and deep neural networks,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks A novel approach for detecting anomalous energy consumption based on micro-moments and deep neural networks,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.645101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1be069d2-aa9a-4189-af89-e67e69c5722f · outbound

This paper cites A deep learning approach for anomaly detection and prediction in power consumption data,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks A deep learning approach for anomaly detection and prediction in power consumption data,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.631591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f336a3ea-e2db-416d-b146-281d4092503b · outbound

This paper cites A deep learning framework for building energy consumption forecast,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks A deep learning framework for building energy consumption forecast,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.618340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0c552f2e-5fd5-4283-965f-5539fd5467fa · outbound

This paper cites Anomaly detection with machine learning al- gorithms and big data in electricity consumption,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Anomaly detection with machine learning al- gorithms and big data in electricity consumption,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.604672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 74987f1f-9c13-4646-902a-a95532ecdd06 · outbound

This paper cites GPT-4 Technical Report.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks GPT-4 Technical Report

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-08T20:30:46.422604Z

Source-reported events for the cited work

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Observation fd7df60e-d1f4-4897-91d5-65da5afaf162 · outbound

This paper cites Forecasting energy consumption demand of customers in smart grid using temporal fusion transformer (TFT),.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Forecasting energy consumption demand of customers in smart grid using temporal fusion transformer (TFT),

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.590264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 046d0a8b-3402-4445-9041-ba0ad35d5109 · outbound

This paper cites A federated learning approach to anomaly detection in smart buildings,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks A federated learning approach to anomaly detection in smart buildings,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.574302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 112ec637-847a-4eff-b32f-c08c1f943e2e · outbound

This paper cites Adversarial examples in the physical world,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Adversarial examples in the physical world,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.559845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5b0a01da-d435-443f-b85a-b6c7612d4e35 · outbound

This paper cites Novel evasion attacks against adversarial training defense for smart grid federated learning,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Novel evasion attacks against adversarial training defense for smart grid federated learning,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.544791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8bff58b4-a8a0-4178-935b-c01a381e89f9 · outbound

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

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Communication-efficient learning of deep networks from decentralized data,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.530093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e47b3d1a-4c83-4fe7-9c42-77954e880f47 · outbound

This paper cites Focal loss for dense object detection,.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Focal loss for dense object detection,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.515108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cb48f3b8-7be8-4b42-9368-7a2744e4aa82 · outbound

This paper cites Available: https://www.unep.org/resources/report/ global-status-report-buildings-and-construction.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Available: https://www.unep.org/resources/report/ global-status-report-buildings-and-construction

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:30:46.964491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

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