Paper Citation Record · LEDGER

Optimization Methods and Software for Federated Learning

As of 13 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2509.08120.

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

pith.paper-citation-record.v1
2509.08120 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T21:20:47.247408Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

16 of 16 outbound references displayed

verified exact0
verified fuzzy1
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 833d35e7-c8a4-4dd7-a525-4f4f508dbbe6 · outbound

This paper cites EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback.

Optimization Methods and Software for Federated Learning EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T21:20:47.188031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 418ca33c-5eb2-4396-b4a2-1665f64a517b · outbound

This paper cites Federated Learning of a Mixture of Global and Local Models.

Optimization Methods and Software for Federated Learning Federated Learning of a Mixture of Global and Local Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T21:20:47.199277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b867f64-10b0-4e9b-a0c6-40fa50618568 · outbound

This paper cites In2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017.

Optimization Methods and Software for Federated Learning In2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T21:20:47.204907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 680245fa-103d-4a05-ba8c-70bae7b80163 · outbound

This paper cites an unresolved cited work.

Optimization Methods and Software for Federated Learning Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-04T21:20:47.664102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7fabfdcb-09ec-46d8-9f8f-1f2e03eae9ba · outbound

This paper cites 70), Doina Precup and Yee Whye Teh (Eds.).

Optimization Methods and Software for Federated Learning 70), Doina Precup and Yee Whye Teh (Eds.)

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-08-04T21:20:47.345394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b3d5e8b5-2e0a-46f7-8a34-426908689080 · outbound

This paper cites an unresolved cited work.

Optimization Methods and Software for Federated Learning Unresolved cited work

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-08-04T21:20:47.317743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c94dedbf-32d4-4520-be22-9749ca4157f0 · outbound

This paper cites InProceedings of the 2020 USENIX Annual Technical Con- ference, USENIX ATC 2020, July 15-17, 2020, Ada Gavrilovska and Erez Zadok (Eds.).

Optimization Methods and Software for Federated Learning InProceedings of the 2020 USENIX Annual Technical Con- ference, USENIX ATC 2020, July 15-17, 2020, Ada Gavrilovska and Erez Zadok (Eds.)

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:20:47.645444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1ccc848a-9c66-4b10-82c8-7b72a98833ce · outbound

This paper cites Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling.

Optimization Methods and Software for Federated Learning Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T21:20:47.247408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd7f3818-e9bd-49be-9b79-b6247c999afc · outbound

This paper cites In40th Annual Symposium on Foundations of Computer Science, FOCS ’99, 17-18 October, 1999, New York, NY, USA.

Optimization Methods and Software for Federated Learning In40th Annual Symposium on Foundations of Computer Science, FOCS ’99, 17-18 October, 1999, New York, NY, USA

Reference 1999

Resolution: metadata mismatch; arxiv_id, observed 2026-08-04T21:20:47.470434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c8774b50-ef7c-4d0e-97e6-a8b03a504a84 · outbound

This paper cites Federated Learning With Quantized Global Model Updates.

Optimization Methods and Software for Federated Learning Federated Learning With Quantized Global Model Updates

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-04T21:20:47.165502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4193532b-7aeb-491d-b65f-be918068dc3b · outbound

This paper cites Distributed Learning with Compressed Gradient Differences.

Optimization Methods and Software for Federated Learning Distributed Learning with Compressed Gradient Differences

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-04T21:20:47.215429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b3543cd-b7fc-4d85-9c25-2678f2a54314 · outbound

This paper cites First Analysis of Local GD on Heterogeneous Data.

Optimization Methods and Software for Federated Learning First Analysis of Local GD on Heterogeneous Data

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-04T21:20:47.209844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1fcccba-c64c-430e-98ba-bbfa41bd36b5 · outbound

This paper cites an unresolved cited work.

Optimization Methods and Software for Federated Learning Unresolved cited work

Reference 2021

Resolution: metadata mismatch; arxiv_id, observed 2026-08-04T21:20:47.527425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 769b0aea-f3ac-4c30-9282-e892935f3246 · outbound

This paper cites an unresolved cited work.

Optimization Methods and Software for Federated Learning Unresolved cited work

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-04T21:20:47.171010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd279884-e259-49cb-92a2-310c57fdc587 · outbound

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

Optimization Methods and Software for Federated Learning Flower: A Friendly Federated Learning Research Framework

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-04T21:20:47.176272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0aac109d-fcdf-4957-a12e-dc2f48f99397 · outbound

This paper cites Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala.

Optimization Methods and Software for Federated Learning Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-04T21:20:47.226549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.