Paper Citation Record · LEDGER

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs

As of 22 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2505.07041.

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

pith.paper-citation-record.v1
2505.07041 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:33:56.224929Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

27 of 27 outbound references displayed

verified exact1
verified fuzzy18
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 512306f8-a410-40f3-8006-a2539d364317 · outbound

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

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Communication- efficient learning of deep networks from decentralized data

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T22:33:56.129620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e34cd8df-55b6-4457-8713-114871207f89 · outbound

This paper cites Advances and open problems in federated learning.Foundations and Trends® in Machine Learning, 14(1–2):1–210, 2021.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Advances and open problems in federated learning.Foundations and Trends® in Machine Learning, 14(1–2):1–210, 2021

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:33:56.531701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e59fcc19-f7ff-44cf-9315-b9e49ce25952 · outbound

This paper cites Balancing privacy and performance in federated learning: a systematic literature review on methods and metrics.Journal of Parallel and Distributed Computing, page 104918, 2024.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Balancing privacy and performance in federated learning: a systematic literature review on methods and metrics.Journal of Parallel and Distributed Computing, page 104918, 2024

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:33:56.519675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3bac4b34-9545-41d6-a8e4-3ff7f1de3a5d · outbound

This paper cites Client selection for federated learning with heterogeneous resources in mobile edge.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Client selection for federated learning with heterogeneous resources in mobile edge

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:33:56.507809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0ef27cc4-a71b-45a4-954b-aa95d6d6e478 · outbound

This paper cites Federated learning with buffered asynchronous aggregation.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Federated learning with buffered asynchronous aggregation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:33:56.496264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1bdd4833-ee53-4d97-b535-3e2d5b2f3b1c · outbound

This paper cites Tifl: A tier-based federated learning system.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Tifl: A tier-based federated learning system

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:33:56.485314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 440d28b0-f332-435e-98ec-ce31dabf424e · outbound

This paper cites Asynchronous Federated Optimization.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Asynchronous Federated Optimization

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T22:33:56.151671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1cdf67fe-3e33-4564-88fb-8329de3deaba · outbound

This paper cites Local differential privacy for deep learning.IEEE Internet of Things Journal, 7(7):5827– 5842, 2019.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Local differential privacy for deep learning.IEEE Internet of Things Journal, 7(7):5827– 5842, 2019

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:33:56.474638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e76e981a-9e88-4b6a-9771-a50eaa9510d3 · outbound

This paper cites Deep learning with differential privacy.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Deep learning with differential privacy

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T22:33:56.159144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 503d1613-88b7-4570-aaff-87f94cd18b09 · outbound

This paper cites Linear queries estimation with local differential privacy.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Linear queries estimation with local differential privacy

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:33:56.457564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 561c2713-fcab-4f1d-adb9-14cc6022d2b4 · outbound

This paper cites A review on speech emotion recognition: a survey, recent advances, challenges, and the influence of noise.Neurocomputing, 568:127015, 2024.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs A review on speech emotion recognition: a survey, recent advances, challenges, and the influence of noise.Neurocomputing, 568:127015, 2024

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:33:56.446798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 33e5ae63-8d73-46c2-bf46-918df5e97117 · outbound

This paper cites Enhancing smart home design with ai models: A case study of living spaces implementation review.Energies, 16(6):2636, 2023.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Enhancing smart home design with ai models: A case study of living spaces implementation review.Energies, 16(6):2636, 2023

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:33:56.435541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f61b1e58-f543-410e-9396-cae56f9515df · outbound

This paper cites Automatic speech emotion recognition using machine learning.Social Media and Machine Learning [Working Title], 2019.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Automatic speech emotion recognition using machine learning.Social Media and Machine Learning [Working Title], 2019

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:33:56.425354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 62cb9709-0eda-4b16-bf38-b2c89a95ce35 · outbound

This paper cites an unresolved cited work.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:33:56.414739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8e1f3c0c-56b3-4d20-baf6-94792620250d · outbound

This paper cites Privacy implications of voice and speech analysis–information disclosure by inference.Privacy and Identity Management.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Privacy implications of voice and speech analysis–information disclosure by inference.Privacy and Identity Management

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:33:56.403925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9ba90074-b024-4fb7-b1c9-8b460689c3e8 · outbound

This paper cites Balancing privacy and accuracy in federated learning for speech emotion recogni- tion.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Balancing privacy and accuracy in federated learning for speech emotion recogni- tion

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:33:56.390619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ad0c7bc0-5a25-4992-ab92-e9a9e5f0d768 · outbound

This paper cites Differentially private federated learning: A client level perspec- tive.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Differentially private federated learning: A client level perspec- tive

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:33:56.379333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 322fd46d-c062-4647-be2b-6516f2c4e590 · outbound

This paper cites Speech emotion recognition with deep convolutional neural networks.Biomedical Signal Processing and Control, 59:101894, 2020.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Speech emotion recognition with deep convolutional neural networks.Biomedical Signal Processing and Control, 59:101894, 2020

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:33:56.367915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2e933d11-7ffe-45f0-9b46-e417f738bac4 · outbound

This paper cites Light-SERNet: A lightweight fully convolutional neural network for speech emotion recognition.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Light-SERNet: A lightweight fully convolutional neural network for speech emotion recognition

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:33:56.293200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7fdc9e47-8f1d-42dd-b17e-d25945e5fe04 · outbound

This paper cites Enhancing emotion recognition through federated learning: A multimodal approach with convolutional neural networks.Applied Sciences, 14(4), 2024.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Enhancing emotion recognition through federated learning: A multimodal approach with convolutional neural networks.Applied Sciences, 14(4), 2024

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:33:56.355905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 256a8791-c460-433f-a9e2-b79407f3b2fe · outbound

This paper cites Personalized federated learning with differential privacy.IEEE Internet of Things Journal, 7(10):9530–9539, 2020.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Personalized federated learning with differential privacy.IEEE Internet of Things Journal, 7(10):9530–9539, 2020

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:33:56.344376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f78ef161-100b-4e33-a32d-d027aeb762a5 · outbound

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

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Flower: A Friendly Federated Learning Research Framework

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T22:33:56.207257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98f2395c-66de-456c-bb2f-6fe0c56d503a · outbound

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

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T22:33:56.210979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58109273-ef2d-4d01-8be4-796dbbcea789 · outbound

This paper cites Crema-d: Crowd-sourced emotional multimodal actors dataset.IEEE transactions on affective computing, 5(4):377–390, 2014.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Crema-d: Crowd-sourced emotional multimodal actors dataset.IEEE transactions on affective computing, 5(4):377–390, 2014

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T22:33:56.214565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b317c947-1028-45a1-9acf-7ec96175e5e7 · outbound

This paper cites Projected federated averaging with heterogeneous differential privacy.Proceedings of the VLDB Endowment, 15(4):828–840, 2021.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Projected federated averaging with heterogeneous differential privacy.Proceedings of the VLDB Endowment, 15(4):828–840, 2021

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:33:56.326123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 072f29ab-c3ba-4c91-8428-638c9f416452 · outbound

This paper cites Online client selection for asynchronous federated learning with fairness consideration.IEEE Transactions on Wireless Communications, 22(4):2493–2506, 2022.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Online client selection for asynchronous federated learning with fairness consideration.IEEE Transactions on Wireless Communications, 22(4):2493–2506, 2022

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:33:56.315063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 85b05c68-55be-47fe-b6da-cfbbecf2227f · outbound

This paper cites Adaptive Personalized Federated Learning.

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs Adaptive Personalized Federated Learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T22:33:56.224929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.