Paper Citation Record · LEDGER

FedFitTech: A Baseline in Federated Learning for Fitness Tracking

As of 9 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2506.16840.

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

pith.paper-citation-record.v1
2506.16840 v2

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:42:55.778948Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

29 of 29 outbound references displayed

verified exact0
verified fuzzy19
unresolved10
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External citation measurements

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Outbound references

Observation a83c2a37-2da7-4803-b6a5-5138bab24829 · outbound

This paper cites A tutorial on human activity recognition using body-worn inertial sensors.ACM Computing Surveys (CSUR), 46(3):1–33, 2014.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking A tutorial on human activity recognition using body-worn inertial sensors.ACM Computing Surveys (CSUR), 46(3):1–33, 2014

Reference 1

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Source-reported events for the cited work

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Observation 26c3be5b-e858-4628-bbcb-c114b6fcab84 · outbound

This paper cites The eu general data protection regulation (gdpr).A practical guide, 1st ed., 10(3152676), 2017.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking The eu general data protection regulation (gdpr).A practical guide, 1st ed., 10(3152676), 2017

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:43:01.044785Z

Source-reported events for the cited work

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Observation 663a874b-ee7e-495a-b8ec-af3842bb36e9 · outbound

This paper cites A guide to the california consumer privacy act of 2018.A vailable at SSRN 3275571, 2018.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking A guide to the california consumer privacy act of 2018.A vailable at SSRN 3275571, 2018

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:43:00.761322Z

Source-reported events for the cited work

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Observation 3ab72b05-dbd8-44aa-afb5-edb7029e0451 · outbound

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

FedFitTech: A Baseline in Federated Learning for Fitness Tracking Communication-efficient learning of deep networks from decentralized data

Reference 4

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Observation f66fb059-8c2e-4ca1-af2b-8fca8738e3f5 · outbound

This paper cites Learning from others without sacrificing privacy: Simulation comparing centralized and federated machine learning on mobile health data.JMIR mHealth and uHealth, 9(3):e23728, 2021.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking Learning from others without sacrificing privacy: Simulation comparing centralized and federated machine learning on mobile health data.JMIR mHealth and uHealth, 9(3):e23728, 2021

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:43:00.404827Z

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Observation fbfdf8b2-9b62-48fb-b648-6c27aced3954 · outbound

This paper cites Evaluation and comparison of federated learning algorithms for human activity recognition on smartphones.Pervasive and Mobile Computing, 87:101714, 2022.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking Evaluation and comparison of federated learning algorithms for human activity recognition on smartphones.Pervasive and Mobile Computing, 87:101714, 2022

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:43:00.221997Z

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Observation ef1d9762-1441-468d-a8cf-872817af45e1 · outbound

This paper cites Fl- pmi: federated learning-based person movement identification through wearable devices in smart healthcare systems.Sensors, 22(4):1377, 2022.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking Fl- pmi: federated learning-based person movement identification through wearable devices in smart healthcare systems.Sensors, 22(4):1377, 2022

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:42:59.954757Z

Source-reported events for the cited work

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Observation 6f528cac-18c8-4d2c-b3f5-e2f467029946 · outbound

This paper cites Meta-har: Federated representation learning for human activity recognition.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking Meta-har: Federated representation learning for human activity recognition

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:42:59.776371Z

Source-reported events for the cited work

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Observation 6752ac43-361f-42fb-9e03-a31a24f896d9 · outbound

This paper cites Protohar: Prototype guided personalized federated learning for human activity recognition.IEEE Journal of Biomedical and Health Informatics, 27(8), 2023.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking Protohar: Prototype guided personalized federated learning for human activity recognition.IEEE Journal of Biomedical and Health Informatics, 27(8), 2023

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:42:59.508352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0f6ceb44-82e7-4755-976d-ad51dcca0fe0 · outbound

This paper cites Flower: A Friendly Federated Learning Research Framework.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking Flower: A Friendly Federated Learning Research Framework

Reference 10

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Source-reported events for the cited work

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Observation 0e249788-02ba-47d0-8cef-02f891932c3b · outbound

This paper cites an unresolved cited work.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking Unresolved cited work

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 510447e3-0191-40d1-9049-ec4276620cb4 · outbound

This paper cites A federated learning system with enhanced feature extraction for human activity recognition.Knowledge-Based Systems, 229:107338, 2021.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking A federated learning system with enhanced feature extraction for human activity recognition.Knowledge-Based Systems, 229:107338, 2021

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:42:58.927338Z

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Observation 56627a29-e4d0-48a0-8cd6-6c8d60e8f38a · outbound

This paper cites 2d federated learning for personalized human activity recognition in cyber- physical-social systems.IEEE Transactions on Network Science and Engineering, 9 (6):3934–3944, 2022.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking 2d federated learning for personalized human activity recognition in cyber- physical-social systems.IEEE Transactions on Network Science and Engineering, 9 (6):3934–3944, 2022

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:42:58.675964Z

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Observation b6600dfc-4d8e-42e6-9199-a8e6194e9f61 · outbound

This paper cites an unresolved cited work.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking Unresolved cited work

Reference 14

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Observation c7198cfc-478c-4adb-8b7a-e3a788f58385 · outbound

This paper cites A profile similarity-based personalized federated learning method for wearable sensor-based human activity recognition.Information & Management, 61(7):103922, 2024.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking A profile similarity-based personalized federated learning method for wearable sensor-based human activity recognition.Information & Management, 61(7):103922, 2024

Reference 15

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Observation 0ffe2919-e5b8-4795-bd37-80804bb597a6 · outbound

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FedFitTech: A Baseline in Federated Learning for Fitness Tracking Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:42:58.157332Z

Source-reported events for the cited work

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Observation 4375659e-3cf6-464a-b03b-2ecbd8e6c789 · outbound

This paper cites Clusterfl: a similarity-aware federated learning system for human activity recog- nition.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking Clusterfl: a similarity-aware federated learning system for human activity recog- nition

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:42:57.974412Z

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Observation 41a70cc1-09a8-435c-9f2c-bc04a79c71ba · outbound

This paper cites Fedclar: Federated clustering for personalized sensor-based human activity recognition.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking Fedclar: Federated clustering for personalized sensor-based human activity recognition

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:42:57.754759Z

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Observation 8d3bfb63-08a9-4002-9cc5-376be5fb00cf · outbound

This paper cites Feddl: Federated learning via dynamic layer sharing for human activity recognition.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking Feddl: Federated learning via dynamic layer sharing for human activity recognition

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:42:57.492435Z

Source-reported events for the cited work

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Observation 45e295a8-45ce-4466-b097-a63aa8bfc1ec · outbound

This paper cites Protecting health monitoring privacy in fitness training: A fed- erated learning framework based on personalized differential privacy.Internet Technology Letters, 7(6):e499, 2024.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking Protecting health monitoring privacy in fitness training: A fed- erated learning framework based on personalized differential privacy.Internet Technology Letters, 7(6):e499, 2024

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:42:57.318051Z

Source-reported events for the cited work

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Observation 0a562ee9-dbe8-4545-adb6-47dc18d1419d · outbound

This paper cites Flrce: Resource-efficient federated learning with early-stopping strategy.IEEE Trans.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking Flrce: Resource-efficient federated learning with early-stopping strategy.IEEE Trans

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:42:57.074764Z

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Observation ce18f8bd-fc45-4ba9-9df7-637604ef16cc · outbound

This paper cites Flash: Concept drift adaptation in federated learning.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking Flash: Concept drift adaptation in federated learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:42:56.937905Z

Source-reported events for the cited work

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Observation e03bfffe-d5ee-457b-a720-d9c3c893111f · outbound

This paper cites Early stopping-but when? InNeural Networks: Tricks of the trade, pages 55–69.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking Early stopping-but when? InNeural Networks: Tricks of the trade, pages 55–69

Reference 23

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Source-reported events for the cited work

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Observation a299b8ff-61ac-44d7-84de-bb25a86e72fa · outbound

This paper cites Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition.Sensors, 16(1):115, 2016.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition.Sensors, 16(1):115, 2016

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:42:56.782871Z

Source-reported events for the cited work

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Observation 2766c668-26a0-4e36-b3be-ba70c87038bc · outbound

This paper cites Ensembles of deep lstm learners for activity recog- nition using wearables.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 1(2):1–28, 2017.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking Ensembles of deep lstm learners for activity recog- nition using wearables.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 1(2):1–28, 2017

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:42:56.542504Z

Source-reported events for the cited work

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Observation e14cc653-a567-47e7-9a37-a5730c944a29 · outbound

This paper cites Tinyhar: A lightweight deep learning model designed for human activity recognition.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking Tinyhar: A lightweight deep learning model designed for human activity recognition

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:42:56.354743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 86115aac-a604-40dd-a953-b9e351499d9b · outbound

This paper cites an unresolved cited work.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:42:56.052893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5c5da559-4918-48e1-8dea-3458f74f2af9 · outbound

This paper cites Beyond Confusion: A Fine-grained Dialectical Examination of Human Activity Recognition Benchmark Datasets.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking Beyond Confusion: A Fine-grained Dialectical Examination of Human Activity Recognition Benchmark Datasets

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T23:42:55.645407Z

Source-reported events for the cited work

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Observation 1f0ddc26-c3ca-4292-9e43-ba96105693de · outbound

This paper cites Demystifying impact of key hyper-parameters in federated learning: A case study on cifar-10 and fashionmnist.IEEE Access, 2024.

FedFitTech: A Baseline in Federated Learning for Fitness Tracking Demystifying impact of key hyper-parameters in federated learning: A case study on cifar-10 and fashionmnist.IEEE Access, 2024

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:42:55.950737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.