Paper Citation Record · LEDGER

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices

As of 20 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2606.11556.

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

pith.paper-citation-record.v1
2606.11556 v1

Coverage vector

measured 45 of 45 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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45 of 45 outbound references displayed

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Outbound references

Observation 062a24fe-bc76-47b3-a624-e885c4f9d336 · outbound

This paper cites Deep Learning with Differential Privacy.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Deep Learning with Differential Privacy

Reference 1

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Observation a09683e9-9fc0-4e50-a9db-38aaa6a0c871 · outbound

This paper cites Federated Learning and Differential Privacy Tech- niques on Multi-hospital Population-scale Electrocardiogram Data.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Federated Learning and Differential Privacy Tech- niques on Multi-hospital Population-scale Electrocardiogram Data

Reference 2

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Observation 2ca4337b-4266-4c66-8a57-27ca3da21e83 · outbound

This paper cites ECG De-Anonymization: Real-World Risks and a Privacy-by-Design Mitigation Strategy.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices ECG De-Anonymization: Real-World Risks and a Privacy-by-Design Mitigation Strategy

Reference 3

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Observation 1fbb653d-6418-46a3-a7c5-ed0f91199ee5 · outbound

This paper cites Unsupervised Transformer-Based Anomaly Detection in ECG Signals.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Unsupervised Transformer-Based Anomaly Detection in ECG Signals

Reference 4

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Observation f9c2bfa2-90c3-48dd-855c-e85f13307c5d · outbound

This paper cites https : / / anonymous.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices https : / / anonymous

Reference 5

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Observation 0a50f6b9-718b-4085-b3bf-68532c95d1e0 · outbound

This paper cites Cryptology ePrint Archive, Paper 2020/704.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Cryptology ePrint Archive, Paper 2020/704

Reference 6

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Observation 9fb52a1b-7fd9-45b7-98a8-0c31ab08ca7c · outbound

This paper cites Federated Disentangled Representation Learning for Unsupervised Brain Anomaly Detection.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Federated Disentangled Representation Learning for Unsupervised Brain Anomaly Detection

Reference 7

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Observation 3073c2bf-8148-4dcd-9572-b6f2b3233c10 · outbound

This paper cites Fusion of Personalized Federated Learning with Differential Privacy for Diagnosis of Arrhythmia Disease.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Fusion of Personalized Federated Learning with Differential Privacy for Diagnosis of Arrhythmia Disease

Reference 8

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Observation c90f9d69-1cff-46e8-a68d-c4e74b28eccc · outbound

This paper cites Federated Learning Strategies for Atrial Fibrillation Detection.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Federated Learning Strategies for Atrial Fibrillation Detection

Reference 9

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Observation ee22d793-e6cf-4f15-8b42-024ce3848713 · outbound

This paper cites Dwork and A.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Dwork and A

Reference 10

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Observation 658d8616-df98-40dd-947b-8114cb4fec0e · outbound

This paper cites Our Data, Ourselves: Privacy Via Distributed Noise Generation.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Our Data, Ourselves: Privacy Via Distributed Noise Generation

Reference 11

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Observation 3e6091f0-de37-450e-8d33-7002f872f947 · outbound

This paper cites Elmir et al.Federated Learning with Gramian Angular Fields for Privacy-Preserving ECG Classification on Heterogeneous IoT Devices.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Elmir et al.Federated Learning with Gramian Angular Fields for Privacy-Preserving ECG Classification on Heterogeneous IoT Devices

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-03T11:08:02.961531Z

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Observation 490d0c25-8dfd-4148-8e5f-7ea6ce316926 · outbound

This paper cites Differentially private federated learning: A systematic re- view.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Differentially private federated learning: A systematic re- view

Reference 13

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Observation add0afe1-53ca-4a89-a190-79b27b289d0d · outbound

This paper cites Garreta Basora and M.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Garreta Basora and M

Reference 14

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Observation 2f41f5df-6655-425a-9780-370cea4585a1 · outbound

This paper cites Privacy-Preserving ECG Data Analysis with Differential Privacy: A Literature Review and A Case Study.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Privacy-Preserving ECG Data Analysis with Differential Privacy: A Literature Review and A Case Study

Reference 15

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Observation 74f7945a-1c00-468c-a88a-5740ff448374 · outbound

This paper cites PhysioBank, PhysioToolkit, and PhysioNet.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices PhysioBank, PhysioToolkit, and PhysioNet

Reference 16

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Observation a872d39d-ce75-418f-bf95-74d8290e1897 · outbound

This paper cites Application of Federated Learning Tech- niques for Arrhythmia Classification Using 12-Lead ECG Signals.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Application of Federated Learning Tech- niques for Arrhythmia Classification Using 12-Lead ECG Signals

Reference 17

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Observation ac8b9476-68df-4bf6-a6b1-6c02451c86d1 · outbound

This paper cites Reliable ECG Anomaly Detection on Edge Devices for Internet of Medical Things Applications.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Reliable ECG Anomaly Detection on Edge Devices for Internet of Medical Things Applications

Reference 18

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Observation 4f10c367-e53a-47d6-9f87-a90beb14a1ca · outbound

This paper cites Federated Learning-Based Privacy-Preserving ECG Signal Classification for IoMT.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Federated Learning-Based Privacy-Preserving ECG Signal Classification for IoMT

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-27T09:42:59.022080Z

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Observation cdcc5e47-1513-43c6-933c-7ba91c6ccf6c · outbound

This paper cites Secure, Privacy-Preserving and Federated Machine Learning in Medical Imaging.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Secure, Privacy-Preserving and Federated Machine Learning in Medical Imaging

Reference 20

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Observation fabc44ab-b6d3-442d-a1ff-8242a96bb0e9 · outbound

This paper cites Cross-device Federated Unsupervised Learning for the Detection of Anomalies in Single-Lead Electrocardio- gram Signals.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Cross-device Federated Unsupervised Learning for the Detection of Anomalies in Single-Lead Electrocardio- gram Signals

Reference 21

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Observation 1f33e4d3-aaa1-4fdc-be4b-5755928688e1 · outbound

This paper cites Auto-Encoding Variational Bayes.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Auto-Encoding Variational Bayes

Reference 22

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Observation 206741ca-8900-4e18-adb9-ffa63fbf2428 · outbound

This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 23

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Observation b194f159-705c-4d21-a67c-070170069ebd · outbound

This paper cites Enhanced Federated Anomaly Detection through Autoencoders Using Summary Statistics-Based Thresholding.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Enhanced Federated Anomaly Detection through Autoencoders Using Summary Statistics-Based Thresholding

Reference 24

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Observation 906e9b52-ce23-4a78-98ff-c16b255cc45e · outbound

This paper cites Communication-Efficient Learning of Deep Net- works from Decentralized Data.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Communication-Efficient Learning of Deep Net- works from Decentralized Data

Reference 25

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Observation 8b9b3f2f-4823-4795-be36-1189384672f4 · outbound

This paper cites Learning Differentially Private Recurrent Language Models.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Learning Differentially Private Recurrent Language Models

Reference 26

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Observation 4a160199-c78e-455e-b7eb-07069fbb811b · outbound

This paper cites Rényi Differential Privacy.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Rényi Differential Privacy

Reference 27

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Observation e3233c7c-3dec-485f-a0c3-4f2e9ff8481b · outbound

This paper cites Anomaly Detection through Unsupervised Federated Learning.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Anomaly Detection through Unsupervised Federated Learning

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-03T11:08:02.972265Z

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Observation 677f08b5-bc5f-4744-a1d5-81e741ea03d7 · outbound

This paper cites Unsupervised ECG Analysis: A Review.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Unsupervised ECG Analysis: A Review

Reference 29

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Observation 2244091a-6014-4e32-837d-162820c5e81c · outbound

This paper cites ECG-DPSHAP: An Approach towards Privacy- Preserving SHAP-Based Explainable AI for 12-Lead ECG Classifica- tion Model.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices ECG-DPSHAP: An Approach towards Privacy- Preserving SHAP-Based Explainable AI for 12-Lead ECG Classifica- tion Model

Reference 30

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Observation afa43228-f352-46a3-84cf-5c81c3a884c1 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 31

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Observation 437a8642-f0d8-4520-9656-fc95fb6309fa · outbound

This paper cites A Secure Multi-Modal Federated Transfer Learning System for ECG Classification.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices A Secure Multi-Modal Federated Transfer Learning System for ECG Classification

Reference 32

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Observation 414a4cb0-9cb6-44e4-86ef-c353da67cdbf · outbound

This paper cites How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy

Reference 33

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Observation ef6f8bcb-d74f-492d-9613-20a83e141afd · outbound

This paper cites Handling Data Heterogeneity in Federated Learning for Medical Imaging: A Review.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Handling Data Heterogeneity in Federated Learning for Medical Imaging: A Review

Reference 34

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Observation 5d126709-5e93-43c1-a9b0-7afaf7935cb0 · outbound

This paper cites Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI

Reference 35

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Observation a632311e-9fff-497d-af06-0d3a949c0ca5 · outbound

This paper cites ECG-based Real-Time Arrhythmia Monitoring Using Quantized Deep Neural Networks: A Feasibility Study.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices ECG-based Real-Time Arrhythmia Monitoring Using Quantized Deep Neural Networks: A Feasibility Study

Reference 36

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Observation 2857aed6-a92d-4e43-aedb-9095b41878d1 · outbound

This paper cites Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL

Reference 37

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Observation b3a9e7c1-1439-48a3-817e-b7ba9be78337 · outbound

This paper cites FEEL-ECG: Federated Edge Learning for Person- alized and Explainable ECG Monitoring with Adaptive Compression and Preprocessing.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices FEEL-ECG: Federated Edge Learning for Person- alized and Explainable ECG Monitoring with Adaptive Compression and Preprocessing

Reference 38

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Observation f47841cd-f62b-4f82-8a62-88d651114086 · outbound

This paper cites PTB-XL, a Large Publicly Available Electrocardio- graphy Dataset.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices PTB-XL, a Large Publicly Available Electrocardio- graphy Dataset

Reference 39

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Observation c2641131-488c-4046-9f7a-bbfab8bd467e · outbound

This paper cites Linkage Attacks Expose Identity Risks in Public ECG Data Sharing.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Linkage Attacks Expose Identity Risks in Public ECG Data Sharing

Reference 40

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Observation 691ca9a0-fd23-4f94-abdd-5c5df71d98a3 · outbound

This paper cites Federated Learning With Deep Neural Networks: A Privacy-Preserving Approach to Enhanced ECG Classification.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Federated Learning With Deep Neural Networks: A Privacy-Preserving Approach to Enhanced ECG Classification

Reference 41

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Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Unresolved cited work

Reference 42

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Observation abf76ef0-69da-4a0a-b76b-7ab572b6a54d · outbound

This paper cites Group Normalization.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Group Normalization

Reference 43

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Observation 1a8f48ec-924a-4e19-8bf5-1958200e1070 · outbound

This paper cites Opacus: User-Friendly Differential Privacy Library in PyTorch.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-07-03T11:08:02.964719Z

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Observation d4e38f1f-b7ac-4a58-92ba-836e3e4bc78d · outbound

This paper cites A Two-Stage Differential Privacy Scheme for FL Based on Edge Intelligence.

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices A Two-Stage Differential Privacy Scheme for FL Based on Edge Intelligence

Reference 45

Resolution: unresolved; no resolver link, observed 2026-06-27T09:42:59.022080Z

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Pith citing papers

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