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Paper Citation Record · LEDGER

On the Convergence of Local Descent Methods in Federated Learning

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:1910.14425.

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

pith.paper-citation-record.v1
1910.14425 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:46:47.592529Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

170
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 5735e215-e8a2-4bc7-b6c9-ab02ee3637a4 · inbound

Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression cites this paper.

Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression On the Convergence of Local Descent Methods in Federated Learning

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-10T15:46:47.592529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T15:46:47.592529Z digest=sha256:2d573d1b0a061552e6958fa6a1a484f6e6362af117786d0e775b859f1a24213f

Observation 3fb4c442-8b3c-48e3-97fa-68224b76677b · inbound

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models cites this paper.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models On the Convergence of Local Descent Methods in Federated Learning

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T13:42:46.062575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:42:46.062575Z digest=sha256:e36a695a9bab4bf13f4a0bff595db3fff2bda77ee58253455f9f4959f2aa214d

Observation f35827fd-fd0f-4179-8445-1cd5184a4a85 · inbound

Adaptive Federated LoRA in Heterogeneous Wireless Networks with Independent Sampling cites this paper.

Adaptive Federated LoRA in Heterogeneous Wireless Networks with Independent Sampling On the Convergence of Local Descent Methods in Federated Learning

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-07T12:52:41.157026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:52:41.157026Z digest=sha256:d1cd7901b42ff1467dbeaac0e87e00a1a7e39d0960d9767daabe91b624b78894

Observation 26c6fc80-2ad1-4e19-b3cc-806255d538f9 · inbound

Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization cites this paper.

Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization On the Convergence of Local Descent Methods in Federated Learning

Reference 82

Resolution
unresolved
no resolver link, observed 2026-08-04T21:06:26.155651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T21:06:26.155651Z digest=sha256:ce6141cb5323488ba02e6d0d62db432437f6ef01b24c0c78faae66ff425d7450

Observation 0b04997b-28b1-4868-aa45-25a18320db7b · inbound

ISAC for AI: A Trade-off Framework Across Data Acquisition and Transfer in Federated Learning cites this paper.

ISAC for AI: A Trade-off Framework Across Data Acquisition and Transfer in Federated Learning On the Convergence of Local Descent Methods in Federated Learning

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-13T05:22:18.970568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-13T05:21:58.697309Z digest=sha256:53d7b1d0684189937703459d908543f0c8df47c8ca46fd2bf9a5de3d2a28a8b9

Observation 37ddac67-f487-4368-81f5-643e0a28ae0c · inbound

Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity cites this paper.

Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity On the Convergence of Local Descent Methods in Federated Learning

Reference 53

Resolution
verified exact
arxiv_id, observed 2026-05-14T19:32:50.921274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-14T19:31:12.149482Z digest=sha256:be5d651d0caa44f29e8e2d58b439dbcccdacd92ff4746a4c80dbf84f59276511

Observation 83ff3c08-8beb-47d4-8487-3ea5fa2fc7e6 · inbound

Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning cites this paper.

Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning On the Convergence of Local Descent Methods in Federated Learning

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-20T08:03:08.757796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-20T08:01:27.080031Z digest=sha256:259be912bd60bbef7e73b1d5cbdb475255246da5a4f72ce4b092a883269ec145

Observation b7d22ddc-746a-4c5b-8899-6eba416e43bd · inbound

Federated Foundation Models Fine-Tuning with Heterogeneous Compressed Clients cites this paper.

Federated Foundation Models Fine-Tuning with Heterogeneous Compressed Clients On the Convergence of Local Descent Methods in Federated Learning

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-03T14:16:09.499421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T14:16:09.499421Z digest=sha256:3d28a335eff8f35647ee88a8c8db57acb670d2a0846155f4ba1c6a73a7b0b9c6