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Paper Citation Record · LEDGER

Federated Learning: Opportunities and Challenges

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2101.05428.

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

pith.paper-citation-record.v1
2101.05428 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:58:50.713353Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T04:17:37.620113Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 34f42d51-0424-4bef-9f1d-a4f27cd8d653 · inbound

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios cites this paper.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Federated Learning: Opportunities and Challenges

Reference 62

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unresolved
no resolver link, observed 2026-08-06T22:58:50.713353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:58:50.713353Z digest=sha256:763f848e9b922e1d885ac6ed30f3082d525dfd5135152cf433a87484b5327931

Observation a39efb73-8ef7-4577-b19f-f785bc68de78 · inbound

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction cites this paper.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Federated Learning: Opportunities and Challenges

Reference 37

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no resolver link, observed 2026-08-06T21:27:04.492438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:27:04.492438Z digest=sha256:5ef58918cd37d09d355e5a65ab804da84f20ef1451d3815840df9da4dd202fe5

Observation 6d050ce3-9d50-4fc7-8ee0-d9c8b8eb03ae · inbound

Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models cites this paper.

Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models Federated Learning: Opportunities and Challenges

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-05T20:33:20.356882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:33:20.356882Z digest=sha256:5fdc5774bcae20da8561dd8823fc07e097464e426946a0594ca8bc564620d94a

Observation f9740531-4216-4c4c-aaf4-1a80d7411e86 · inbound

A Robust Framework for Secure Cardiovascular Risk Prediction: An Architectural Case Study of Differentially Private Federated Learning cites this paper.

A Robust Framework for Secure Cardiovascular Risk Prediction: An Architectural Case Study of Differentially Private Federated Learning Federated Learning: Opportunities and Challenges

Reference 40

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no resolver link, observed 2026-08-02T19:59:25.281641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T19:59:25.281641Z digest=sha256:f8eed532b18ebca53ba709b148c68a0609a53f8b9991f018ab14bb75d7f02e79

Observation 46b57aae-2a20-4230-a73c-1b28570e478b · inbound

Privacy-Preserving Federated Learning via Differential Privacy and Homomorphic Encryption for Cardiovascular Disease Risk Modeling cites this paper.

Privacy-Preserving Federated Learning via Differential Privacy and Homomorphic Encryption for Cardiovascular Disease Risk Modeling Federated Learning: Opportunities and Challenges

Reference 20

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metadata mismatch
arxiv_id, observed 2026-05-12T10:06:30.268153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-07T07:42:16.699861Z digest=sha256:27710c3c21c3a6cbff8b22f7b38ddc6af349881a7dcaca2ca983a10d9436505f

Observation 3db7d0a6-780b-4baa-9315-e9b900f2f15e · inbound

Federated Distillation for Whole Slide Image via Gaussian-Mixture Feature Alignment and Curriculum Integration cites this paper.

Federated Distillation for Whole Slide Image via Gaussian-Mixture Feature Alignment and Curriculum Integration Federated Learning: Opportunities and Challenges

Reference 39

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verified exact
arxiv_id, observed 2026-05-21T00:13:52.808161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-21T00:13:11.388212Z digest=sha256:c94d4e46586d63452955744c7cbb53c577ecf74bdf988cbf2bed1050748035c0

Observation 881aff64-b28b-42f1-8e58-3f64b46aba5b · inbound

Stable Localized Conformal Prediction via Transduction cites this paper.

Stable Localized Conformal Prediction via Transduction Federated Learning: Opportunities and Challenges

Reference 57

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verified exact
arxiv_id, observed 2026-05-11T16:16:09.514230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-09T18:11:12.792341Z digest=sha256:9139d5b76b3b183a739e9dd22db9ff5c43f7279a41951558323a74914b93a939

Observation 47629418-5216-4458-9c61-87a910109f9b · inbound

Demystifying the Optimal Fair Classifier in Multi-Class Classification cites this paper.

Demystifying the Optimal Fair Classifier in Multi-Class Classification Federated Learning: Opportunities and Challenges

Reference 82

Resolution
verified exact
arxiv_id, observed 2026-06-28T19:52:35.612586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-28T18:49:29.377237Z digest=sha256:752777f3fb7aa8020dc0604c887351b16da43a9ef27b33023962ebf4130dd604

Observation 0711630c-f909-48cc-bf66-e306e743725b · inbound

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning cites this paper.

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning Federated Learning: Opportunities and Challenges

Reference 2

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T22:36:17.095996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T15:17:22.294338Z digest=sha256:57a44ce5d90f6c31143168fd3a2e8c2aa31f0ca30c2632d0577545f35ebd5189

Observation 657f8396-464b-4222-9fb7-fa1dc6109fa3 · inbound

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning cites this paper.

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning Federated Learning: Opportunities and Challenges

Reference 2

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no resolver link, observed 2026-07-14T18:32:49.291467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T18:32:49.291467Z digest=sha256:c6fb53c9125dcb30a38ce573a34839287dfd9b517b32903802b5f515e917236b

Observation a900373b-c22b-4957-9336-d966bd660cb1 · inbound

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning cites this paper.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Federated Learning: Opportunities and Challenges

Reference 76

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T08:46:49.036906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T05:46:08.181285Z digest=sha256:a813d7f87a7d3baa2a1d16753914fe32b7eef87649c1ad66124fe6c273c11c62

Observation fa774c64-4e50-4ff3-b16e-9170b0c01e19 · inbound

Enhanced localized conformal prediction with imperfect auxiliary information cites this paper.

Enhanced localized conformal prediction with imperfect auxiliary information Federated Learning: Opportunities and Challenges

Reference 53

Resolution
verified exact
arxiv_id, observed 2026-07-02T23:47:27.362032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-27T18:00:17.793161Z digest=sha256:5f73e809aae1d11b0e90af2956987b7b2b2232adbea7e50faf6f26043daf212b

Observation 1731b4fe-d6dc-447e-8483-a544fdc89d97 · inbound

Asynchronous Decentralized Federated Learning over Lossy Wireless Links via Reception- and Age-Aware Aggregation cites this paper.

Asynchronous Decentralized Federated Learning over Lossy Wireless Links via Reception- and Age-Aware Aggregation Federated Learning: Opportunities and Challenges

Reference 3

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metadata mismatch
arxiv_id, observed 2026-07-03T04:17:37.622097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-27T13:59:25.075112Z digest=sha256:3fde84869d030e15d0ecd9c5bd16df23521fd11d09e1871459a214737a25a61d

Observation 06bf7f1a-bfe5-41d2-862b-4c0c8bead38f · inbound

What Your Model Threw Away and Why You'll Want It Back: Masking, Fingerprinting, and Privacy from Discarded Geometry cites this paper.

What Your Model Threw Away and Why You'll Want It Back: Masking, Fingerprinting, and Privacy from Discarded Geometry Federated Learning: Opportunities and Challenges

Reference 28

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unresolved
no resolver link, observed 2026-08-02T11:05:27.873595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T11:05:27.873595Z digest=sha256:3aabc73c622d84922dd286ea8d0f289e601e9a81d5d00054afe13b24139bc8f0

Observation c93bc5de-54fd-41a4-8f20-ecc464c477ca · inbound

Sarus: Privacy-Preserving Multi-Vendor Perception Fusion via Homomorphic Encryption cites this paper.

Sarus: Privacy-Preserving Multi-Vendor Perception Fusion via Homomorphic Encryption Federated Learning: Opportunities and Challenges

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-01T13:23:28.058204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T13:23:28.058204Z digest=sha256:83e4e62706617b1b7d6bce07e8ea1e2827c31f8810975a0a32d9f0be54146048